What is your role in the team/project you lead? / What is your team’s composition?

  • My team is part of the Generative AI org within AWS. We’re a centralized org with the charter of building GenAI models in collaboration with other business units such as Amazon Shopping, Alexa, Amazon Prime Video, and Amazon Music. Our goal is the adoption of GenAI for projects and initiatives across Amazon.
  • I support a team of 16x recursive reports with 7x Applied Scientists (responsible for model building), 4x Data Scientists (data pre-processing and evaluation pipelines), 3x Deep Learning architects (productionizing models), and 2x Managers (with 1x in training).
  • Overall: 16x (11 direct reports incl. managers)
  • We split the work between these two managers based on job function – one with scientists and one for infrastructure building.

Tell me about a time that a peer or manager gave you specific, actionable feedback for improvement.

  • Story 1:
    • Situation:
      • In my role supporting a Generative AI team at AWS, we were in the middle of developing “Rufus,” the conversational shopping chatbot on Amazon.com. As part of this development effort, my team was tasked with developing a LLM that followed strict safety and compliance protocols.
      • These measures were crucial to ensure the chatbot met Amazon’s user safety standards and legal regulations, especially given the project’s customer-facing nature—Rufus currently serves millions of users daily. As the manager responsible for the project, I collaborated closely with Amazon’s legal and compliance team to review each feature’s alignment from a legal perspective and eventually obtain legal approval for Rufus’s launch.
    • Task:
      • In my communication with the legal team, I had already been simplifying complex concepts to make them understandable for non-technical stakeholders, proactively addressing their concerns, and connecting the dots between the project’s details and its broader impact for the legal team. However, I realized that some of my messages still lacked clarity and structure, which occasionally led to misunderstandings. In my 1:1s with a peer manager, she suggested using the AIM (Audience, Intent, Message) framework as a tool to better structure the conversation and make my messaging even more effective for the legal audience.
    • Action:
      • After reflecting on this feedback, I restructured my approach using the AIM framework. First, I considered the Audience—the legal and compliance team—and focused on addressing their specific regulatory and risk concerns, using their terminology and jargon to bring about clearer alignment. I clarified my Intent: obtaining approval by demonstrating how our solutions aligned with legal standards and the company’s broader goals. For the Message, I outlined how each feature adhered to specific regulatory requirements, mitigated legal risks, and directly contributed to overarching business objectives, such as maintaining customer trust and reducing liability.
      • For instance, when explaining the impact of our efforts in setting up adequate guardrails for Rufus, I framed it in terms of regulatory compliance, using familiar terminology to highlight how these safety measures aligned with data privacy regulations and content moderation standards, reducing the chances of the chatbot generating harmful or biased responses. Additionally, I linked this to business objectives, such as safeguarding customer trust by preventing potential reputational damage and ensuring a safe, reliable user experience that supports Amazon’s customer-first philosophy.
    • Result:
      • This made the discussions more productive and streamlined the approval process, as they could see/grasp the direct link between Rufus’s performance on safety benchmarks and adherence to the company’s legal and business objectives. As a result, the legal team approved the project on schedule, and this approach became a model for me to leverage for future XF collaboration, especially with non-technical partners.
    • Reflection:
      • In hindsight, this experience reinforced the importance of (i) embracing feedback wherever possible, and (ii) having the right tools in your toolbox. In this case, it taught me the value of the AIM framework to tailor my communication effectively by bridging technical and non-technical perspectives, making complex discussions with diverse audiences clearer and more aligned with their priorities.
  • Story 2:
    • Situation:
      • Over the past 5 years, I’ve built closely knit teams at Apple and Alexa, but when I transitioned to AWS, I faced a new challenge of managing a fully remote team distributed country-wide/across two continents for the first time.
      • As a manager, the importance of camaraderie and cohesion in building strong interpersonal relationships in the team was clear to me, so I used to schedule regular virtual coffee chats and team-building activities like game days to create informal touchpoints for the team.
      • At Amazon, we use anonymous connection scores to evaluate several key aspects of team dynamics, such as inclusion, job satisfaction, manager effectiveness, technical skills, and alignment with company culture. A noticeable drop in team effectiveness became a critical signal that deeper issues were affecting the team’s cohesion and performance.
      • The challenge I faced was that with a remote team distributed country-wide, fostering a sense of camaraderie and connection, as well as cultivating a sense of belonging, required intentional effort beyond virtual touchpoints.
    • Task:
      • During this time, I had a conversation with a peer manager who had faced a similar challenge managing a remote team.
      • She empathized with my situation and offered me a nugget of wisdom from her own experience.
      • She suggested that while virtual touchpoints like coffee chats were helpful, it was worth using part of the budget to invest in in-person events such as team on-sites and fairs (similar to a science fair but focused on demoing our team’s work) to connect people in person and celebrate team achievements.
      • Her rationale was that while virtual touchpoints are valuable, in-person events can significantly accelerate team cohesion and trust.
      • The issue was that I inherited the team from a different manager who had not allocated any budget for in-person events during the planning process at the beginning of the year.
    • Action:
      • I worked closely with my director, created projections for the required funds, and secured some last-minute budget reshuffling to make these events possible.
      • In addition to the virtual coffee chats, I hosted in-person team on-sites (inviting key XF partners) and a team fair to demo our work and celebrate achievements together.
      • These face-to-face experiences helped the team connect on a deeper level, fostering stronger relationships beyond project deadlines.
    • Result:
      • The in-person events brought a new level of cohesion to the team.
      • Over the next couple of months, I noticed a significant shift in how team members interacted, both internally and with XF partners.
      • Communication improved, trust deepened, and collaboration became more fluid and efficient.
      • The team fair reinforced pride in our work and provided a unique platform to engage with XF partners.
      • These efforts created bonds that continued to yield dividends in collaboration, trust, and performance well beyond the events.
    • Reflection:
      • This experience reinforced the belief that “feedback is a gift”, and being open to new strategies can lead to lasting improvements when faced with challenges.
      • Lastly, this investment in building a cohesive team not only improved morale but also yielded long-term dividends in collaboration and project success.

What were some considerations for technical and non-technical stakeholders based on the AIM framework?

  • For Technical Audience:
    • Audience: Engineers, developers, and technical stakeholders.
    • Intent: Emphasize technical innovation, solving complex issues, and performance improvements.
    • Message:
      • Detailed breakdown of architecture, algorithms, and metrics.
      • Diagrams and data flow visuals to explain how it works.
      • Impact on efficiency, system stability, or technical scalability.
  • For Non-Technical Audience:
    • Audience: Executives, product managers, business stakeholders.
    • Intent: Show alignment with business objectives, user benefits, and competitive advantage.
    • Message:
      • High-level overview, focusing on user impact and business goals.
      • Projections on user engagement, revenue potential, or strategic advantages.
      • Simplified visuals or case studies to illustrate user benefits.

Tell me about a time you had to pivot/take a detour mid-project due to project requirements or stakeholder expectations changing. / Tell me about a project you were on that grew in scope and timeline in an unexpected way. / Tell me about a time you didn’t have desired resources to complete a project and/or deliverable because scope increased.

  • Story 1:
    • Situation:
      • In my role supporting the personalization team at Amazon Alexa, we were in the middle of building a year-end recap experience, similar to how FB shows you memories from the past: “this time last year”.
      • The goal was to list some of the major items users bought during the year and proactively recommend products that users could purchase via Alexa, taking advantage of the peak shopping period during Christmas holiday season.
      • This was a critical project since Christmas is typically the busiest shopping period of the year, and providing personalized product recommendations could significantly boost customer engagement and drive growth for our shopping business.
      • My team was responsible for building a recommendation model that suggested products based on users’ shopping interests. As part of this, we worked closely with the Amazon.com catalog team – which owned the user data and product catalog data – to ensure the recommendations were tailored to each user.
      • Midway through the project, the scope shifted due to a request from the VP of Amazon Entertainment.
    • Task:
      • The new requirement was to go beyond retail products and expand the system to highlight the music and movies users had enjoyed throughout the year and suggest what to listen to or watch next.
      • The strategy was to deliver shopping and music recommendations to users through Alexa, and movie recommendations via the Alexa app, personalized emails, or on-screen for Alexa devices with built-in displays.
      • This pivot aligned with our business objective of increasing customer engagement, especially during the Christmas season, when both shopping and entertainment consumption are at their peak.
      • Given that this was a year-end holiday season project, we had a strict deadline to deliver by the end of the year. This detour threatened to throw the project off-track, but pursuing it made sense due to the potential for significantly boosting business metrics during the high-volume Christmas season.
    • Action:
      • Given that this was a significant pivot, I worked closely with my team to evaluate how this scope expansion would affect our timeline and priorities.
      • Communicating the need for additional resources given the increased scope and technical complexity, I collaborated closely with a Senior Manager within Amazon Entertainment to secure two data scientists and engineers to help integrate the music and video recommendation features.
      • Using the RICE framework for prioritization, I worked with my PM to re-do sprint planning by identifying tasks that could be deferred to future sprints and low-priority tasks that could be moved to our backlog, to accommodate the additional effort required. This led to a delay of 2 weeks beyond our initially planned timeline.
      • With the additional support, we focused on ensuring that users’ interaction history (shopping preferences, music preferences, and movie rentals) could inform not only shopping suggestions but also personalized music and video recommendations.
    • Result:
      • The end result was that despite the mid-project change in direction, we successfully met the year-end deadline and delivered an enhanced recommender system that suggested both retail products as well as music and video content.
      • This not only helped us exceed the original goals, but significantly boosted customer engagement during the high-traffic holiday season.
    • Reflection:
      • Looking back, this experience reinforced the importance of agility and nimbleness, especially when working on large-scale projects with evolving requirements. The ability to pivot quickly (in this case, as a result of changing stakeholder expectations) while maintaining strong communication across XF teams was critical to our success.
  • Story 2:
    • Scope of GenAI model changed.
      • Initially was looking at a few shot prompting solution for a client who needed explainability in their billing methodology for their customers.
      • Turns out their domain was very niche and needed RAG with additional documents to help explain the math: discounted pricing for lower income customers etc
      • Started with Reviews -> Rufus
      • Showed to have big impact, big partnership, thus we accommodated

Describe a time when it was challenging to obtain support from others for an important initiative or project.

  • Story 1 (XF had a layoff so couldn’t support):
    • Situation:
      • At Amazon Alexa, where I supported query understanding and personalization, we were leading a critical project to release a multimodal transcription model for the Echo Show, an Alexa device with a built-in display with a camera. The goal was to address performance gaps in query understanding seen with the prior speech-only transcription model while transcribing commands in a noisy environment—for instance, in a busy kitchen with multiple sources of background noise—by integrating both audio and visual signals for more accurate speech transcription. To set some context, query understanding is one of the initial steps in Alexa’s flow, since it maps user commands to a set of over 200 intent APIs and their arguments that control all of the device’s use-cases, from shopping to playing music to setting reminders to controlling smart home devices. However, given that query understanding was a gate for downstream models in Alexa’s flow, errors at this stage can easily snowball/propagate/cascade further to models further on in the query’s lifecycle/flow. As a result, this initiative was strategically important as it directly impacted user experience and retention.
      • While our offline evaluation showed that the multimodal model was able to successfully address the performance gaps compared to the speech-only model, given that this was the first time we were experimenting with multimodal transcription coming from speech-only transcription, we realized that the latency projections that we used for model planning were inaccurate.
      • This led to our latency numbers being ~12% off compared to our initial projections, which exceeded our latency requirements. The challenge here was that these were strict to ensure seamless, real-time user interactions with the device – missing these targets would lead to users waiting inordinately long to get a response back from Alexa, leading to a poor user experience.
      • To address this, we required the Edge AI team’s expertise to hit the necessary latency targets within performance constraints. This involved trying out various model compression techniques such as quantization, pruning, knowledge distillation and figuring out the right trade-offs to make between performance and latency.
      • However, the Edge AI team recently had a reduction in headcount due to org restructuring leading to a packed slate of priorities for the quarter and thus couldn’t allocate resources to support our needs. This jeopardized the launch of the model, which was critical to the success of the Echo Show device.
    • Task:
      • My task was to secure the necessary support from the Edge AI team to help us compress the model and meet the required latency targets to ensure the success of our project.
    • Action:
      • To address the issue, I scheduled a 1:1 with the Edge AI team’s manager to discuss the situation and identify potential ways forward. I approached the conversation with empathy, acknowledging their limited resources and busy slate of priorities. I discussed the business impact of our project and its importance to the overall success of the Echo Show device. While the manager agreed that the project was important, she explained that due to their recent headcount reductions, they were strapped for resources and that their current resources were fully committed to other initiatives, and they couldn’t provide direct support this quarter. Without their help, shipping this project seemed like a distant dream since violating latency requirements was out of question given the real-time nature of the device.
      • To keep the ball rolling on the project, I proposed a middle ground where the Edge AI team would offer (i) knowledge sharing sessions on the infrastructure and best practices for model compression and (ii) help us with code reviews to ensure our implementation was functionally correct and met performance standards, allowing my team to continue making progress.
      • Meeting-in-the-middle enabled us to move forward without further delays while maintaining a positive and collaborative relationship for future projects.
    • Result:
      • Although the project’s success was initially at risk due to the latency issues, meeting-in-the-middle allowed us to meet our latency targets and launch the model on schedule.
      • Despite the challenges, we maintained a positive and cooperative working relationship with the Edge AI team, which was crucial for future collaborations.
    • Reflection:
      • Looking back, this experience taught me the importance of finding a middle ground to turn a challenging situation such as this one into a win-win. Above all, it highlighted that empathy and effective communication are some of the best tools in a manager’s toolbox to build and maintain strong long-lasting relationships.
  • Story 2 (Unsupportive XF):
    • Situation:
      • Leading a Critical Alexa Project: In my prior team at Amazon Alexa, where I supported query understanding and personalization, we were leading a critical project to release a multimodal model for the Echo Show, an Alexa device with a built-in screen, aimed at enhancing query understanding by transcribing the speech query using both audio and visual signals. This feature was essential for driving user growth and improving user experience.
      • Dependence on XF Support: The success of this feature depended heavily on support from a XF team responsible for managing the infrastructure needed for efficient model deployment.
      • Repeated Deprioritization: Despite prior agreements, the XF team consistently deprioritized our requests, stalling the project. Attempts to reach out via Slack and email were met with silence.
      • Ongoing Delays: Week after week, sprint after sprint, the infrastructure-related tasks were backlogged, causing significant delays and bringing the project to a standstill. These delays threatened key milestones and jeopardized the overall success of the project.
      • Frustration Over Lack of Support: Although collaboration had been agreed upon, the ongoing lack of XF support left me frustrated and unable to move the project forward.
    • Task:
      • Securing XF Team Support: My task was to secure the necessary support from the XF team to unblock our infrastructure dependencies and ensure the timely delivery of our multimodal model.
      • Navigating Conflicting Priorities: I needed to navigate the XF team’s priorities without creating friction or damaging relationships.
      • Emphasizing Business-Critical Nature: Additionally, I had to ensure that the business-critical nature of our project was recognized and aligned with the broader company objectives, making it clear that this project was a priority for the org.
    • Action:
      • I took a couple of steps to address the issue while maintaining a positive and collaborative relationship with the XF team, ensuring future cooperation was not compromised.
        1. 1:1 with XF Team’s Manager: I did a 1:1 with the XF team’s manager, approaching the situation with empathy and acknowledging their competing priorities. I focused on framing the discussion around the end-user impact of our project, explaining the importance of the task and how we could align this project with both our team’s goals. I brought up how our project could have a positive impact on overall customer engagement, which would also be beneficial for both teams. However, the manager mentioned that they had re-shuffled some of their goals for this quarter and at this point our goals were misaligned – their focus for this quarter was solely on feature development for their infrastructure pipelines and they did not have bandwidth to support any other goals at this time. While I empathized with his resource constraints, despite all these efforts, the 1:1 unfortunately didn’t lead to any tangible progress. I let them know that given that this was core to our business as one of flagship projects, we should seek support from senior leadership.
        2. Escalation to Director: I escalated to his manager, the director of the org, and had a 1:1 with him. With a neutral tone, I explained the situation and acknowledged their lack of resources, while emphasizing the mutual benefits of the project for both teams.
        3. Meeting in the middle: Through constructive discussions, we reached a meet-in-the-middle solution that allowed both teams to move forward. The XF team agreed to providing a series of detailed knowledge-sharing sessions on the infrastructure requirements, enabling my team to take on the integration responsibilities ourselves. To accommodate this, we have to deprioritize certain tasks. In return, the XF team agreed to review our code to ensure it aligned with their standards. This approach allowed us to maintain a positive and cooperative relationship while ensuring the project progressed without further delays.
    • Result:
      • Empathy and Flexibility Led to a Solution: By approaching the situation with empathy and flexibility, we reached a solution that worked for both teams.
      • Successful Project Delivery: The end result was that we successfully completed our half of the equation, while the XF team worked on their half, allowing us to deliver the project on time.
      • Maintained Positive Working Relationship: Despite the initial friction, we maintained a positive working relationship with the XF team, which was vital for future collaborations.
    • Reflection:
      • Importance of Empathy, Communication, and Compromise: In hindsight, this experience reinforced the value of empathy, communication, and compromise to find win-win solutions, especially when working with teams with competing priorities.
  • Story 3:
    • Situation: In my prior team at Amazon Alexa, we had a very discovered intent detection LLM latency v performance
    • Task: This was a very important project fix that we needed as it was critical.
    • Action: What we proposed was to use an LLM as it can help, however we had a latency vs performance issue as this had to run realtime
    • Result
    • Reflection

Tell me about the most difficult working relationship that you’ve had.

  • Situation:
    • At Apple, my team worked closely with a IC who had recently transitioned into a management role. While he had great technical expertise, his leadership style created challenges for the entire team. He adopted a highly authoritarian approach to leadership, often taking a “my way or the highway” approach. He resisted his team’s input during project planning, would frequently push back on collaboration requests from XF teams, leading to significant project delays and tension. He also overruled his team’s suggestions for improving internal processes, which made team members felt their voices weren’t heard and they had little agency in decision-making. This dampened team morale, making it difficult for us to work with his team cohesively.
    • For instance, during the development of a conversational search engine for Siri, which was aimed at allowing Siri to generate synthesized responses rather than simply displaying search results, my team identified potential usability issues. When users asked Siri questions like, “Siri, when is the best time to go for a walk today?” the results relied heavily on a lexical/keyword-based search, which meant that a difference in phrasing could yield inconsistent or irrelevant results. We proposed developing a hybrid search system that incorporated both lexical and semantic elements to improve answer relevance. However, he dismissed our feedback without discussion, insisting on the existing approach. This led to last-minute changes when our concerns were later validated, resulting in a significantly increased workload as the team scrambled to implement last-minute changes that could have been addressed proactively.
  • Task:
    • My role was to maintain team productivity and foster a positive work culture despite the growing challenges. I also needed to address XF partners’ concerns about our team’s perceived lack of cooperation. Establishing open communication and a collaborative atmosphere was crucial to meeting project goals and preventing further delays.
  • Action:
    • I approached him with constructive feedback, highlighting specific instances of how his actions impacted team dynamics. For instance, I shared how dismissing design feedback had led to increased stress for the team and extended timelines. I encouraged him to be more inclusive in his leadership, emphasizing the benefits of considering team input and collaborating with XF partners.
    • To mitigate the immediate impact on team morale, I regularly checked in with my team members during 1:1s to ensure their concerns were being heard, and I shared this feedback with senior leadership. This helped leadership better understand the challenges we were facing and explore possible solutions. In response, they arranged management coaching sessions to help him improve his leadership approach. However, despite multiple rounds of feedback and coaching, his management style remained inflexible, which continued to disrupt team cohesion. Ultimately, leadership made the decision to transition him back to an individual contributor role, where he could excel without the responsibilities of managing others.
  • Result:
    • After he transitioned back to an individual contributor role, the team’s productivity and morale significantly improved. Collaboration with XF teams became smoother, and we were able to meet project deadlines more effectively without the previous friction. The team felt more empowered and valued, contributing to a healthier and more collaborative work environment.
  • Reflection:
    • In retrospect, this experience reinforced the importance of effective leadership and the impact it has on team dynamics. It highlighted that aligning roles with individuals’ strengths is crucial for maximizing both their own performance as well as the overall effectiveness of the team.
    • Lastly, this experience also taught me how crucial it is to provide constructive feedback and advocate for team members to ensure a productive work environment.

Tell me about a time when you faced pushback regarding your approach on a project / Tell me about a time when you observed an inter-team conflict. What did you do?

  • Story 1:
    • Situation:
      • At Amazon Alexa, where I supported the query understanding and personalization team, Alexa’s “Let’s Chat” mode was one of our flagship projects. To set some context, Alexa’s Let’s Chat mode is powered by an LLM and allows users to engage in open-ended, natural conversations with Alexa. The vision for this feature was to broaden the range of topics Alexa could handle, offering more intuitive and context-aware responses, with the ultimate goal of improving customer engagement as a key business metric. While offline testing revealed great benchmark performance, feedback during dogfooding was that the model showed gaps in performance for certain low-resource languages such as Farsi, Tamil, and Malayalam. Digging into some of these samples, we noticed that the model exhibited hallucinations which were evident based on signals such as when users interrupted Alexa mid-way through a response, explicitly pointed out errors in her responses, or asked follow-up questions like “are you sure?”.
      • I worked closely with the Titan team (Titan is Amazon’s foundation LLM, similar to Meta’s Llama) and spearheaded an initiative for addressing these gaps and enhancing the performance of this feature. The primary issue was that the existing model was not grounded in a sufficiently large corpus of customer dialogues encompassing a variety of intents, languages, and use-cases.
      • I proposed deploying a model which was fine-tuned on a more diverse and expansive dataset that covered a broader range of low-resource languages across various real-world use cases and dialogue contexts. However, the caveat was this model was a 12B parameter model, 50% larger than the current 8B model. This model was a significant upgrade in performance from the existing 8B model, based on initial tests conducted by my team which showed substantial improvements in accuracy and a reduction in hallucinations. However, the challenge was that while the 8B model met the latency requirements, the 12B model was about 7% beyond the maximal permissible latency. Since the LLM’s output would be passed as input to a text-to-speech (TTS) model for generating the final speech response, I encountered pushback from the TTS team since this additional latency would eat into their latency budget, which would impact the overall user experience. This brought our launch to a standstill, requiring immediate resolution to move forward.
    • Task:
      • My next step was to come up with a plan to satisfy both objectives: improving the performance of Alexa’s Let’s Chat mode by deploying a larger model trained on more diverse data, while simultaneously addressing the latency concerns raised by the TTS team to ensure an optimal user experience.
    • Action:
      • I worked closely with the TTS team to collaboratively find a point on the performance vs. latency graph that worked was acceptable to both teams. As part of this discussion, I communicated the importance of accuracy for the end-user experience by showing examples of critical failure scenarios, while also acknowledging their concerns about latency. To figure out an acceptable trade-off point, I proposed that we explore model compression techniques to reduce latency while maintaining as much performance as possible.
      • Using the RICE framework for prioritization, I worked with my PM to re-do sprint planning by identifying tasks that could be deferred to future sprints and low-priority tasks that could be moved to our backlog, to accommodate the additional effort required. This approach allowed us to focus on implementing model compression techniques—such as pruning, knowledge distillation, and quantization—similar to those used in training smaller models like Llama 3.2 1B and 3B, to reduce the model’s footprint with minimal performance trade-off.
    • Result:
      • The end result was that we identified a middle ground that satisfied both constraints – addressing the performance gaps within the latency constraints. While the model took a minor performance hit, it was still a significant improvement over the previous version, and the latency was kept within acceptable thresholds.
    • Reflection:
      • Looking back, this experience highlighted the importance of finding a middle ground to transform a challenging situation into a win-win. It also highlighted/underscored/reinforced the need of effective communication and empathy as tools to balance competing priorities—such as performance and latency—and build and maintain strong working relationships.
  • Story 2:
    • Faced pushback on a XF project
    • My approach was to parallelize the work on both teams, any components that we could and get to dependencies later
    • Pushback was from other team, they did not have bandwidth and wanted to wait for our entire task to finish to then take it over
    • Found a middle ground, had them working 30% of time and gave knowledge transfer to our folks for rest

    • Situation:
      Conflict between teams over infrastructure delays: In one project, we depended on a XF team for infrastructure updates, but they deprioritized our requests, causing frustration within my team and delaying key milestones.

    • Task:
      Addressing the conflict and unblocking progress: My task was to resolve the conflict and ensure that both teams could work together productively to meet the project’s deadlines.

    • Action:
      Engaging the PM of the other team: I initiated a direct conversation with the PM from the other team to understand their priorities and explain how their delays were affecting our project. Escalating when necessary: When the initial discussions didn’t result in progress, I escalated the issue to their director and suggested a phased integration plan that would allow both teams to make progress without further delays. Maintaining regular syncs: I set up weekly syncs between both teams to ensure ongoing alignment and to address potential blockers early.

    • Result:
      Restoring collaboration and project progress: The conflict was resolved, and the phased integration allowed both teams to meet their objectives. Our project was delivered on time, and the working relationship between the teams improved.

    • Reflection:
      Importance of proactive conflict resolution: Looking back, this experience reinforced the importance of addressing conflicts early and finding compromises that benefit both teams. Structured communication is key: Regular communication helped keep both teams aligned and prevented future conflicts.

Did this lead to a delay in shipping timelines?

  • Yes, it did. To meet our planned deadlines, my team re-did sprint planning for the next two sprints and deprioritized some of our lower priority tasks to accommodate the effort involved here.

Why is A/B testing necessary despite achieving improved scores in offline evaluation?

  • A/B testing remains critical even after achieving improved scores in offline evaluation because offline tests, while valuable, do not fully capture the complexity of real-world environments. Here’s why:

    1. IID Assumption Doesn’t Hold: Offline evaluation typically relies on the assumption of independent and identically distributed (IID) data. However, this assumption may break when the model is deployed. In a real-world environment, the data is influenced by various factors such as user interactions, changing behaviors, and external influences that don’t appear in offline test data. For example, a new ranking model might alter user behavior, meaning the data seen post-deployment is no longer distributed in the same way as in training.

    2. Unmodeled Interactions / Interactive Effects: In an online setting, there could be interactions between different elements, such as stories/ads or products, that were not accounted for in the offline evaluation. A new model might produce unforeseen effects when deployed, leading to interactions that negatively impact user experience or performance, even though offline metrics improved.

    3. Non-Stationarity / Staleness of Offline Evaluation Data: The data in a real-world environment often changes over time (non-stationarity). User preferences, trends, and behaviors can shift, causing staleness of the data that the model was offline-evaluated on. This, in turn, renders a model that performed well in static, offline tests less effective in a dynamic online environment.

    4. Network Effects / Feedback Loops: When deploying models in an interconnected system like social media or e-commerce, network effects may arise. For instance, the introduction of a new ranking model may lead to a feedback loop where user behavior affects the content that is surfaced or highlighted, which in turn affects user behavior. This complexity isn’t captured in offline evaluations and requires A/B testing to detect and understand.

    5. Data Leakage: Data leakage can occur in multiple ways, leading to an overestimation of the model’s performance during offline evaluation. Two common scenarios are:
      • Training Data Present in Test Data: Data leakage can happen if the training data is inadvertently included in the test set. In this case, the model might be evaluated on data it has already seen during training, artificially boosting its performance metrics. This happens because the model is effectively being tested on known data, rather than unseen data, which inflates its apparent accuracy and generalizability.
      • Model Trained on Test Data: Another form of data leakage occurs when test data is mistakenly included in the training set. This allows the model to learn from the test data before it is evaluated, leading to misleadingly high performance during offline evaluation. In deployment, however, the model will fail to generalize properly to new, unseen data, as it has become reliant on patterns from the test data that would not be available in a real-world scenario.
      • While the model may appear to perform well in offline tests due to these forms of leakage, its true performance may be far worse in a live environment. A/B testing helps uncover these issues by providing a realistic measure of performance without relying on flawed offline evaluations.
    6. Potential Data Issues: There might be hidden issues such as biases in the training and offline evaluation data that don’t manifest until the model is deployed at scale. A/B testing can reveal such problems by comparing real-world performance with expectations derived from offline evaluation.
  • Thus, while offline evaluation is useful for initial model validation, A/B testing in a live environment is essential to fully understand how the model performs in practice. It helps to capture complexities like user interactions, feedback loops, dynamic environments, and potential issues such as data leakage that cannot be simulated effectively in offline tests.

Tell me about a time when you were caught in the middle of a conflict or disagreement between two of your team members. / Tell me about a time when you observed an intra-team conflict.

Here’s the version with the important parts of each bullet within each STARR section highlighted in boldface:

  • Situation:
    • A Senior Staff-level engineer recently joined our team from a different company and expected daily, hands-on guidance from the junior engineers to help him ramp up on Amazon’s complex systems and processes.
    • During his onboarding project, the Senior Staff engineer frequently asked junior engineers to sit with him and walk him through code step-by-step, rather than reviewing available documentation and learning independently. This lack of proactiveness not only created tension but also made the process inefficient, as the junior engineers, who were already managing full workloads, found these demands too time-consuming. They were expecting a more proactive approach, where they could offer support by addressing specific questions rather than being expected to provide continuous, in-depth training.
    • The onboarding plan that I had setup for the Senior Staff engineer already included comprehensive documentation, training materials, and pointers to curated resources to help him gain the necessary background for his new role, yet he was not leveraging these effectively.
    • This growing misalignment in expectations led to frustration: the Senior Staff engineer felt unsupported, while the junior engineers became overwhelmed by the additional demands on their time.
  • Task:
    • As the team’s manager, I was responsible for addressing the misalignment between the Senior Staff engineer’s expectations for training and the junior engineers’ capacity to provide support.
    • My goal was to resolve the growing tension and ensure that both parties could collaborate effectively. Even though I wasn’t directly involved in the training, it was essential for me to step in to prevent further friction and improve team dynamics.
  • Action:
    • To gather insights, I held 1:1 meetings with both the Senior Staff engineer and the junior engineers to understand their individual perspectives. I learned that the Senior Staff engineer was frustrated by his slow onboarding progress and wanted more guidance, while the junior engineers were feeling overburdened by constant requests. I reassured the junior engineers that I recognized their concerns and was committed to finding a more sustainable way to balance their workload.

    • In my conversation with the Senior Staff engineer, I provided feedback that Amazon encourages a self-directed approach, especially for senior employees. This means that they were expected to dig into the initial pointers provided in the onboarding documentation and resources, and learn independently, while leaning on other engineers for support.

    • To support both sides, I ensured the junior engineers were no longer expected to provide constant, on-demand help but instead committed to scheduled Q&A sessions at designated times each week. This allowed for focused support without disrupting their day-to-day workflow.

    • I scheduled regular check-ins with the Senior Staff engineer to track his progress and ensure he was adapting to the new environment. I also checked in with the junior engineers to ensure that their workloads were balanced and their support remained effective.

  • Result:
    • The conflict was successfully resolved as the Senior Staff engineer adapted to Amazon’s self-directed culture and made significant progress by using the resources provided and participating in the scheduled Q&A sessions.
    • The junior engineers appreciated the structure provided by the scheduled Q&A sessions, which allowed them to manage their work more efficiently without feeling overwhelmed by constant interruptions.
    • Overall, team collaboration improved, and the onboarding process for the Senior Staff engineer progressed smoothly without further disruptions to the team’s workflow.
  • Reflection:
    • This experience highlighted the importance of providing timely, actionable, and constructive feedback to keep the team aligned and foster a culture of productivity and cohesion.

Tell me about a time when you had to adopt an experimental approach to resolving something.

  • Project: (might be intended for project manager not people manager)
    • Situation:
      • At Amazon Alexa, where I supported the query understanding and personalization team, we were tasked with introducing shopping recommendations for Alexa, specifically for voice commands like “Alexa, let’s shop for leather jackets.”
      • Unlike mobile or web recommendations, Alexa’s voice interface can only suggest a couple of items at a time, making it crucial to get the top suggestions right.
      • Our hypothesis was thus that the current mobile/web shopping ranking model might not perform optimally in a voice-only context because voice interactions limit the number of items that can be presented at once. Unlike visual interfaces where users can glance at multiple suggestions, voice interactions focus user attention on a single response, making it essential to surface the most relevant item right away. Additionally, voice users expect faster, more direct answers, without the ability to scroll or compare multiple options, requiring a different ranking prioritization.
    • Task:
      • The goal was to come up with an experimental approach to develop a ranking model tailored for the unique challenges of Alexa’s voice interface, providing relevant, personalized suggestions despite the constraints of voice-only interaction.
    • Action:
      • To validate our hypothesis, we began by testing the baseline model to understand the specific performance and latency gaps in a voice-only environment.
      • On top of the shopping recommender system’s candidate retrieval output, we implemented a ranking model that offered personalized suggestions based on Alexa-specific signals (such as the identity of the speaker, location of the device within the household, etc.) to address the unique challenges of voice interactions (beyond the user, item, and contextual features typically used in ranking models).
      • The key challenge was designing a model that met Alexa’s strict latency requirements, as Alexa is a real-time voice assistant and any delay in response could negatively impact the user experience. To address this, we trained and evaluated seven different models with various architectures, including simple LTR models (pointwise, pairwise, and listwise), as well as wide and deep, deep and cross, and deep and cross V2, to balance performance and latency trade-offs.
      • To evaluate the performance-latency trade-off, we conducted extensive offline testing for each model to measure the ranking quality (using metrics such as mAP and NDCG) and response time (measured in ms) across a variety of shopping queries. The result was a ranked list of models that met the latency requirements, sorted by performance.
      • Post offline evaluation, we rolled out the top 3 candidates internally to dogfood them before we sent them for online testing.
      • The top 2 candidates from offline testing moved forward to A/B testing where we compared the baseline model as a control with the new models as treatments to evaluate their performance in a live environment. In addition to key success metrics like user engagement and conversion rates, we tracked guardrail metrics such as opt-out rates and dialogue latency. This data-driven approach allowed us to determine which models provided the best customer experience, balancing performance and latency for Alexa’s voice interface.
    • Result:
      • The experimental approach we adopted led to the development of a new ranking model specifically optimized for Alexa’s voice interface. It personalized suggestions based on user identity and context, significantly improving upon the baseline model.
      • The new model resulted in higher user engagement, increased customer satisfaction scores, and improved conversion rates compared to the baseline.
    • Reflection:
      • Looking back, this project reinforced the value of starting with a hypothesis and testing it using a data-driven, experimental approach by iteratively refining our approach based on real-world feedback.
  • People:
    • Situation:
      • I had an engineer on my team who aspired to switch job families from Software Engineer (SWE) to Machine Learning Engineer (MLE). The engineer was primarily responsible for building infrastructure and productionizing models, and had done exceptionally well in that capacity. Their motivation stemmed from a desire to build expertise in model training and deepen their understanding of machine learning and deep learning algorithms, rather than the infrastructure and deployment aspects of the workflow. While they had the right motivation and interest, my responsibility as their manager was to ensure they were set up for success in making this transition.
    • Task:
      • My goal was to establish a structured yet flexible process that would support their transition while helping them achieve their career goals and aspirations. The process was experimental because it was the first time we were transitioning someone from an infrastructure-focused role to a machine learning role, with no established guidelines in place.
    • Action:
      • I gave a heads-up to senior leadership and got buy-in from them for this experimental approach, ensuring that the process would have support at all levels.
      • I put together a quarter-based comprehensive development program with OKRs, having two key components: a foundational theoretical component and a practical component. The foundational component was designed to ensure the engineer had the necessary theoretical background in ML while the practical component focused on building the required practical skills via real-world projects where the engineer could apply the theoretical knowledge gained. This blend of theory and practice was critical for them to pick up the necessary skills, with regular check-ins along the way to assess progress and provide guidance creating a feedback loop at every step. This created a strong support system for the engineer, ensuring that the engineer could successfully transition into their new role.
      • As part of the foundational component, I provided the engineer with a series of my ML fundamental primers that are publicly available on aman.ai, covering topics such as learning paradigms (supervised/self-supervised/unsupervised), training neural networks, model evaluation metrics, etc.. Alongside these primers, I also offered them a curated list of ML resources, including pointers to key research papers, online courses, and tutorials. These resources were aimed at deepening their knowledge in machine learning and helping them build the necessary skills for the transition.
      • To build a strong practical skill-set atop their theoretical foundations, the practical program involved shadowing projects where the engineer spent 20% of their bandwidth on diverse ML projects—spanning training and fine-tuning various types of models, model evaluation, and other core tasks. This gave them real-world exposure to MLE work.
      • After shadowing, the engineer finally gained hands-on experience, contributing more actively to ML projects. Eventually, they dedicated 80% of their bandwidth to ML tasks while still allocating 20% of their bandwidth to their original SWE role.
      • To finalize the transition, I initiated a round of 360-degree feedback, gathering input from the team to ensure everyone was aligned and supported the formal transition.
    • Result:
      • The end result of this approach was that it proved effective, as the engineer successfully transitioned into the MLE role. Since then, they have delivered a variety of high-impact ML projects, including improving model accuracy and optimizing deployment pipelines, demonstrating strong technical growth. Their contributions have not only enhanced team performance but also positively influenced key business metrics.
    • Reflection:
      • Looking back, this experience reinforced the value of adopting an experimental approach when solving complex challenges, especially in talent development – a key job function for managers. By combining theoretical and hands-on experience with regular feedback loops, I created a structured process that helped not only this engineer but could also be used for future transitions.

Tell me about a time a leader asked you to do something that you didn’t view as the highest priority.

  • Story 1:
    • Situation:
      • In my role supporting a GenAI team at AWS, my director asked me to take on a new initiative: conducting monthly demos of our ongoing work for XF teams. The purpose of these demos was to increase visibility into our progress, allowing other teams to gain a deeper understanding of our approach and provide real-time feedback on potential integration points.
      • While I acknowledged the value of collaboration, I initially didn’t view this as a high priority. We were already sharing design documents, final project outcomes, and regular status updates with these teams. These existing documents provided comprehensive insights into our technical specs, solution architectures, and key performance metrics, which I felt should suffice to keep all teams aligned. However, leadership saw these live, interactive demos as a way to foster more dynamic conversations, surface integration challenges earlier, and ensure cross-team alignment throughout the development process.
      • At that time, I believed that preparing monthly demos would divert valuable time and resources from the actual implementation work. My concern was that given our tight deadlines, I preferred that our team maintain focus on delivering tangible product features rather than crafting demos and presentations.
    • Task:
      • Despite my reservations, my task was to organize and lead these monthly demos, ensuring they were informative and addressed leadership’s goal of improving XF alignment and buy-in.
    • Action:
      • Although I initially had concerns about the time commitment, I realized that my manager, with a vantage point that allows him a broader perspective of the organization’s needs, was focused on ensuring smoother collaboration and preempting potential integration challenges. By understanding their vantage point, I recognized that their request was not just a reporting task but a strategic move to address issues that might not have been visible to my team, ultimately benefiting our long-term success.
      • I shifted my approach and organized detailed presentations and demos that not only showcased the final outcomes but also walked XF teams through our thought process, interim steps, and the rationale behind decisions. I ensured that each demo highlighted both progress and challenges, inviting feedback from other teams at critical development stages.
    • Result:
      • The end result was that the demos turned out to be more beneficial than I initially expected. By presenting our incremental progress and thought process, we fostered better alignment across stakeholders and identified potential integration challenges earlier, helping preempt issues.
      • Eventually, these demos helped us gain better buy-in from XF teams, leading to smoother collaboration on future XF initiatives.
    • Reflection:
      • Looking back/in hindsight, this experience reinforced the importance of understanding leadership’s broader/long-term perspectives that can foster a cohesive culture and stronger XF collaboration, even when a proposed idea didn’t seem relevant in the short-term.
  • Story 2:
    • Leadership asked me to set up midway checkpoints with XF teams.
    • We generally do checkins early in the project (to get all technical buy in. This includes what we are building end to end, the cost, the owners everything)
    • They have a general good picture about it
    • Then we take off and our engineers continue to collaborate, but the leadership team will meet again at the point of integration/testing
    • Leadership wanted midway check in which didn’t seem like a priority
    • However it did lead to less conflicts later on as sometimes theres a disconnect b/w theoretical design and literal implementation and this was a good way to do a temperature check

Tell me about a time when you volunteered to take on an important portion of a critical project. / Tell me about a time when you took ownership of a project that was initially outside of your scope. / Tell me about a time when you took initiative to complete an important project for your team.

  • Story 1:
    • Situation
      • At Amazon Alexa, where I supported query understanding and personalization, I was leading a project to develop the next generation of the semantic intent recognition for Alexa, powered by quantized LLMs. Intent recognition enables is a crucial component in creating a seamless interaction experience since it maps user commands to a set of over 200 intent APIs and their arguments that control all of the device’s use-cases, from playing music to setting reminders or controlling smart home devices. The new model represented a significant shift from the existing implementation, which relied on a combination of shallow models and rule-based methods. The idea behind the project was to enhance Alexa’s intent parsing ability across multiple devices, languages, and a wide range of contextual variations, improving Alexa’s capacity to accurately interpret user commands on devices worldwide.
      • The QA team was responsible for ensuring that the system could accurately recognize and respond to user commands across all these variables. Given the complexity of the feature, comprehensive testing was critical to validate its accuracy, adaptability, and robustness. This required testing both automated scenarios and manual, multi-intent interactions to ensure the feature worked seamlessly in real-world environments. Without this thorough testing, there was a risk of failure in key use-cases, impacting the overall user experience.
      • Unfortunately, just as we were entering this critical testing phase, the QA team experienced a major setback. They lost two of their most senior members within a week—one left the company, and another had to take an unexpected medical emergency leave. This left the team severely understaffed especially with a packed slate of other priorities for the quarter, and they were suddenly unable to carry out the necessary comprehensive testing to certify this project. This situation posed a significant risk to both the launch timeline and the quality of the new feature.
    • Task
      • Since delays in testing had jeopardized the project’s timeline, my responsibility was to come up with a mitigation plan to ensure that the comprehensive testing of our new Alexa feature—across the matrix of multiple devices crossed with languages crossed with intents—was completed on schedule despite the QA team’s staffing issues.
    • Action
      • I immediately reached out to the QA team’s manager to assess their remaining capacity and re-prioritize the testing tasks we had planned earlier. Together, we identified the most critical aspects of the testing plan that required immediate attention.
      • Looking at the sprint plan, I worked closely with members of my team to identify the tasks we could de-prioritize and have them help with some of the testing tasks for this project. To ramp up my team members, I requested the QA manager to arrange knowledge-sharing sessions led by their team members to provide insights on their testing infrastructure and automation frameworks. This allowed us to distribute some of the testing load and have my team to contribute to the testing plan.
      • Simultaneously, I coordinated with senior leadership to bring in resources from a different org’s QA team to support the project, focusing on the more complex multi-intent scenarios that required manual attention.
    • Result
      • Using a combination of re-prioritizing tasks, cross-training my team, and utilizing external QA members, we ensured that the new semantic intent recognition system for Alexa was fully vetted before launch.
      • As a result of this strategy, we were able to keep the project on track without significantly compromising the quality of the product.
    • Reflection
      • This experience reinforced the importance of flexibility and finding a middle ground (or agility or nimbleness or adaptability) to achieve win-win situations, especially in the face of a significant challenge.
  • Story 2: (VJ, I think this story is good, but there should not be a different team doing A/B testing. Maybe lets change it to the fallback workflow implementation?)
    • Situation:
      • One of the flagship projects my team at Amazon worked on was Rufus, a conversational shopping chatbot designed to enhance the customer experience by enabling users to interactively shop using voice or text. The project was critical, as it had the potential to significantly improve customer engagement, leading to a lift in customer growth—a key business objective. A crucial component of the project was conducting online (A/B) testing to evaluate Rufus’s online performance before deployment. However, A/B testing was planned to be handled by a parallel team, which faced resource constraints due to layoffs, putting the overall project at risk.
      • Additionally, my team was working against a packed development roadmap with several major milestones. We had to deliver the chatbot through multiple phases, including V0 (MVP), V1 (RAG Model), and V2 (Guardrails, multilingual support for over 17 geos). Each milestone brought its own set of challenges and required careful planning to ensure timely delivery without compromising on quality.
    • Task:
      • With the A/B testing team facing resource constraints, I recognized that the success of Rufus—and its subsequent deployment—depended on the timely and thorough evaluation of its online performance. This was critical to ensuring the chatbot delivered on key business metrics like customer engagement, conversion rates, and average order value (AOV).

      • Additionally, the complexity of conducting A/B testing across 17 geos added another layer of difficulty. The testing needed to account for different user behaviors across regions, and the data collection and analysis from such a large-scale deployment became a herculean task. We needed to ensure consistent and reliable insights from the tests despite these geographic challenges.

    • Action:
      • I took the initiative to lead my team in taking on the A/B testing responsibility, even though it was originally assigned to the other team, ensuring the chatbot would be thoroughly tested before launch to prevent surprises in the wild. I re-prioritized my team’s workload to accommodate this new challenge, despite it being outside our original scope.
      • To effectively measure Rufus’s online performance, we developed a comprehensive A/B testing strategy. This strategy included monitoring the aforementioned key metrics such as click-through rate (CTR), conversion rate, and average order value (AOV), which would help us understand how well the chatbot was driving business outcomes. Additionally, we identified guardrail metrics like page load time, customer opt-out rate, and customer satisfaction (CSAT) scores, ensuring that while optimizing for business growth, we didn’t negatively impact the user experience.
      • To ensure high-quality testing, I engaged one of our senior data scientists with experience in A/B testing and experimental design. His expertise helped guide the process of setting up test scenarios, data collection, and result analysis, ensuring we maintained rigor in our testing approach.
      • I worked closely with him to address potential technical and performance issues that could arise during the A/B testing phase. We maintained weekly checkpoints to ensure alignment and to quickly resolve any issues.
      • Given that this was a highly XF project (with 5-6 teams), throughout the project, I facilitated clear communication with stakeholders and leadership, ensuring alignment on our testing plans and the chatbot’s deployment schedule.
    • Result:
      • By taking on the online testing component, my team was able to deliver a well-tested Rufus chatbot, ensuring it met both performance and customer engagement goals. The A/B tests built significant confidence in the chatbot’s performance, leading to a successful launch.
      • The thorough testing and successful deployment of Rufus significantly improved customer engagement metrics, directly contributing to Amazon’s growth objectives.
      • Our ability to step in and handle tasks outside our domain helped maintain project momentum and strengthened cross-team collaboration, setting a positive precedent for future projects.
    • Reflection:
      • This experience taught me the importance of taking initiative, being adaptable and resourceful, even if it means stepping outside one’s comfort zone. This led to a win-win scenario and above all, reinforced the importance of being a team player.
  • Story 2:
    • Our team in AWS is customer-facing and we make GenAI solutions for their use cases.
    • We have a sister team that works on these customers’ recommendation system pipeline.
    • We had a new customer come in that needed both GenAI RAG pipeline, as well as a ranking layer that ranks the output.
    • This was a mix of both the teams’ efforts.
    • However, our sister team could not take it up, so we took this critical portion of the project even though it was out of domain.
    • This came in from my manager who is a director. This is key to the business because it targeted the most important business metric—growth.
    • My team had a principal scientist who was a subject matter expert in GenAI and RecSys. He had a great background in both areas.
    • So we freed up his bandwidth to take up this project as it was critical for the success of the business.
    • Critical for the success of the overall org.

    • Situation:
      • My team at AWS focuses on building Generative AI solutions for various use cases. We have a sister team that typically handles recommendation system pipelines for the same customers. A new customer came in with a high-priority request: they needed both a GenAI RAG (Retrieval-Augmented Generation) pipeline and a ranking layer to order the outputs. Typically, ranking is not a GenAI problem statement. This was a mix of work handled by both teams and critical because it aligned with one of the most important business metrics—growth.
    • Task:
      • Due to resource constraints, our sister team was unable to take on the ranking pipeline of the project. Despite this being outside our typical domain, my manager asked us to take on the ranking system development to ensure the success of the overall project, which was critical for the org’s growth and success.
    • Action:
      • Recognizing the importance of this project, I volunteered to lead our team in taking on this responsibility, even though it was outside our usual scope.
      • To ensure we had the right expertise, we freed up the bandwidth of our principal scientist, who had strong knowledge in both GenAI and recommendation systems. His expertise was essential to bridging the gap between the two domains and ensuring we delivered a seamless solution.
      • I collaborated closely with him to define the approach for integrating the RAG pipeline with the ranking layer. We structured the work, divided responsibilities, and made sure we had clear checkpoints to keep the project on track.
      • Additionally, I facilitated communication with the customer to ensure their needs were being met throughout the project.
    • Result:
      • By taking on this critical portion of the project, we were able to deliver a solution that integrated both the GenAI RAG pipeline and the ranking layer, ensuring the customer’s needs were fully met.
      • This project not only helped us retain a key customer but also contributed to the business’s growth objectives, as it became a showcase for our capabilities in handling complex, cross-domain solutions.
      • The success of this project strengthened our relationship with the customer and demonstrated our team’s ability to step outside our usual domain to deliver impactful results.
    • Reflection:
      • This experience underscored the importance of flexibility and stepping up when the situation calls for it, even if it involves areas outside of your immediate expertise.
      • It also reinforced the value of leveraging internal expertise—like our principal scientist—in critical moments to ensure success.
      • Volunteering for this task allowed me to contribute to the business’s most important objectives and showcase our team’s versatility and problem-solving abilities.
  • Story 3:
    • I like to take ownership as a manger
    • We had an internal project come in that would help us standardize our A/B testing platform/ policies
    • Our engineers/ principal engineers/ leads had their plates full
    • This was critical, w/o A/B testing, we do not know which model to release
    • I took it on myself to help standardize this specific thing in A/B testing so now everyone uses it
  • Story 4:
    • Situation:
      • While managing the applied science team for Alexa, the infrastructure supporting our machine learning models was becoming a bottleneck due to increased user demand. This was a critical issue, but the responsibility of updating the infrastructure was not within my immediate scope.
    • Task:
      • I realized that this issue would directly affect my team’s ability to scale our models, so I volunteered to lead the infrastructure overhaul, even though it was officially owned by another engineering team.
    • Action:
      • I initiated a cross-team collaboration, bringing together engineers from infrastructure and applied science to assess the problem.
      • Led efforts to define new system requirements for better scaling and optimization, aligning them with the team’s needs for future model development.
      • Took responsibility for creating a project plan that outlined phased updates to the infrastructure without disrupting ongoing projects.
    • Result:
      • The infrastructure was successfully upgraded, leading to 30% faster model training times and improved system reliability.
      • This proactive effort helped ensure that our upcoming features were delivered on time, and it strengthened cross-team collaboration.
    • Reflection:
      • This experience showed me the value of stepping up when critical issues arise, even if they are outside your direct responsibility. Taking ownership of the project not only solved an urgent problem but also strengthened my ability to lead XF initiatives.

Tell me about a time that you had to explain a technical concept to a non-technical audience. / Describe a time when you had to modify your communication style or approach to interact effectively with others from a different background.

  • Story 1:
    • Situation:
      • In my role supporting a GenAI team at AWS, one of the flagship projects my team at Amazon worked on was Rufus, a conversational shopping chatbot powered by LLMs, designed to enhance the customer experience. Rufus aimed to help users interactively shop using voice or text as well as answering product-related questions based on customer reviews, which was critical for improving customer engagement and driving customer growth—a key business objective.
      • To handle diverse customer inquiries about specific products effectively, we needed to deploy Rufus with a Retrieval-Augmented Generation (RAG) pipeline to pull relevant data from product reviews and provide accurate, context-rich responses.

      • Task: To obtain legal approval for Rufus’s launch, it needed to meet a variety of complex legal standards. As the manager responsible for the project, my responsibility was to ensure our technical solutions and product choices aligned with these standards.

      • Action: To ensure effective communication with non-technical stakeholders, I employed the AIM framework (Audience, Intent, Message). By tailoring my explanation to the specific needs of the marketing and finance teams (Audience), my Intent was to get their buy-in by demonstrating how our solutions aligned with legal standards and the company’s broader goals. I crafted my Message to outline how each feature adhered to specific regulatory requirements, mitigated legal risks, and directly contributed to overarching business objectives, such as maintaining customer trust and reducing liability. For instance, when explaining the impact of our efforts in setting up adequate guardrails for Rufus, I framed it in terms of regulatory compliance by highlighting how these safety measures aligned with data privacy regulations and content moderation standards, reducing the chances of the chatbot generating harmful or biased responses. Additionally, I linked this to business objectives, such as safeguarding customer trust by preventing potential reputational damage and ensuring a safe, reliable user experience per Amazon’s customer-first philosophy.

      • Result: This made the discussion more productive and streamlined the approval process, as they could see/grasp the direct link between Rufus’s performance on safety benchmarks and adherence to the company’s legal and business objectives. As a result, the legal team approved the project on schedule, and this approach became a model for me to leverage for future XF collaboration, especially with non-technical partners.

      • Reflection: In hindsight, this experience reinforced the importance of having the right tools in your toolbox. In this case, it taught me the value of the AIM framework to tailor my communication effectively by bridging technical and non-technical perspectives, making complex discussions with diverse audiences clearer and more aligned with their priorities.
  • Story 2:

    • Situation:
      • One of the flagship projects my team at Amazon worked on was Rufus, a conversational shopping chatbot powered by LLMs (Large Language Models), designed to enhance the customer experience. Rufus aimed to help users interactively shop using voice or text as well as answering product-related questions based on customer reviews, which was critical for improving customer engagement and driving customer growth—a key business objective.
      • To handle diverse customer inquiries about specific products effectively, we needed to deploy Rufus with a Retrieval-Augmented Generation (RAG) pipeline to pull relevant data from product reviews and provide accurate, context-rich responses.
      • To execute on this plan, it was essential to secure alignment and support from two non-technical stakeholders: marketing and finance. Marketing needed to understand and support the technical approach to effectively publicize Rufus’s capabilities to customers, while finance was crucial for cost approval to ensure the RAG pipeline implementation received the required resources. Getting buy-in from them was essential because they influenced the resources and prioritization needed to move forward with the RAG implementation.
    • Task:
      • When engaging with non-technical teams – in this instance, the marketing and finance teams – my task was to explain why building a RAG pipeline was essential for Rufus to answer product-related questions based on customer reviews.
    • Action:

      1. To ensure effective communication with non-technical stakeholders, I employed the AIM framework (Audience, Intent, Message). By tailoring my explanation to the specific needs of the marketing and finance teams (Audience), my Intent was to convey the business relevance of implementing the RAG pipeline, and I crafted my Message to focus on the big picture impact and emphasize how it would enhance customer satisfaction, sales, and conversion rates, using clear, relatable terms to align with their interests. Specifically, I framed the conversation around how customer experience and engagement would improve by providing more accurate, relevant product information through RAG, ultimately driving sales and repeat purchases.

      2. Next, distilling the concept of RAG, I explained that RAG allows Rufus to “search through product reviews in real time” and incorporate that information into its responses, acting like a “smart assistant” that uses real customer feedback to improve accuracy. Without RAG, Rufus would rely on outdated or generic information, but with RAG, it could dynamically retrieve the latest reviews, providing personalized and accurate responses that would enhance the shopping experience.

      3. I tried to proactively anticipate some of the stakeholder’s concerns such as response accuracy, latency, scalability, and highlighted how RAG would result in quicker, more accurate responses, leading to a projected 15% increase in customer satisfaction and a 10% boost in conversion rates and the ability to scale easily with the growing number of reviews, keeping responses up-to-date without requiring manual updates.

    • Result:
      • The end result was that the team fully supported the RAG pipeline, understanding how it would improve our key business metrics of customer satisfaction and growth.
      • After launch, Rufus saw a lift in business metrics inline with our expectations.
    • Reflection:
      • In retrospect, this experience reinforced the importance of explaining technical changes in terms of business impact and focusing on the metrics that matter to non-technical stakeholders.
      • I also learned the value of anticipating potential concerns from the audience and proactively addressing them in my communication to build confidence and ensure smoother buy-in from stakeholders.
  • Story 2:

    • AIM framework.
    • Intern vs Director vs Customer.
    • Project was our first launch in the GenAI org.
    • Australian Open multi-modal AI tennis umpire.

    • Situation:
      • Developing a Multi-Modal AI Umpire for the Australian Open: I was working on a high-profile project in our GenAI org: developing a multi-modal AI umpire for the Australian Open. This system leveraged both vision and language models to make real-time decisions on tennis matches, such as line calls and scorekeeping.
      • Diverse Stakeholders: The project involved collaborating with a diverse group of stakeholders, from junior engineers and interns to senior directors and external customers, each with different levels of expertise and communication needs.
    • Task:
      • Ensuring Effective Communication and Collaboration: My task was to ensure effective communication and collaboration across this diverse group.
      • Adapting Communication for Different Audiences: I needed to adapt my communication style to accommodate interns who were learning the ropes, directors who needed high-level overviews and strategic insights, and external customers who were concerned with the practical applications and benefits of the AI umpire in enhancing the tennis tournament experience.
    • Action:
      • Using the AIM Framework: I used the AIM framework (Audience, Intent, Message) to tailor my communication.
        • For Interns: I broke down the complex vision and language models into manageable concepts and focused on teaching, making sure they understood both the technical details and their role in the project. I emphasized mentorship, offering guidance to help them grow within the project.
        • For Directors: I presented high-level overviews, focusing on strategic alignment and the broader impact of the AI umpire on Amazon’s GenAI strategy and partnerships. I omitted the granular technical details and instead concentrated on business metrics, such as market potential and the innovative edge the system would bring.
        • For Customers: When interacting with customers, I shifted my focus to outcomes. I explained how the AI umpire would enhance the Australian Open experience by increasing accuracy in line calls and providing real-time feedback. I translated technical jargon into user benefits, such as improved fairness in matches and better engagement for spectators.
    • Result:
      • Strong Buy-In and Alignment from Stakeholders: By modifying my communication style according to the audience, I was able to secure strong buy-in and alignment from all stakeholders.
      • Interns’ Effective Contributions: The interns felt supported and contributed effectively.
      • Directors’ Confidence in Strategic Direction: The directors were confident in the strategic direction.
      • Customer Excitement About Impact: The customers were excited about the potential impact of the AI umpire.
      • Successful Launch: This alignment was crucial to the successful launch of the multi-modal AI umpire at the Australian Open, which received widespread praise for its accuracy and innovation.
    • Reflection:
      • Tailoring Communication for Different Stakeholders: This experience highlighted the importance of tailoring communication to meet the needs of different stakeholders.
      • AIM Framework for Effective Communication: By using the AIM framework, I was able to ensure that everyone, regardless of their technical background or role, understood the project’s value and their part in its success.
      • Adaptability Key to Success: This adaptability was key to the project’s successful deployment and reception at a global event.

Tell me about a time when you needed to act quickly on something but did not have a clear idea on how to best proceed.

  • Story 1:
    • Situation:
      • At Amazon Alexa, where I supported the query understanding and personalization team, we were tasked with launching a new feature called kids mode for Alexa that enforced kid-specific guardrails (such as shopping restrictions, no explicit music, etc.). To set context, Alexa, being as a communal device, is used by various members of the household and is personalized to their preferences, creating the need for accurate user recognition to ensure appropriate personalization and content delivery.
      • Given that this was a new initiative, there were several unknowns about how the model would behave in diverse real-world environments with multiple voices, accents, and background noise variations. These factors introduced edge cases that were challenging to anticipate fully during pre-launch testing.
      • Prior to launch, we had carried out our due-diligence with offline evaluation on a new curated, human-annotated dataset to validate performance and robustness and subsequent online A/B testing. Despite these efforts, in a couple of weeks after launch, we received a couple of tickets that were triaged to Alexa misrecognizing children as adults and delivering personalized recommendations intended for adults. This misclassification effectively lifted the protective guardrails Alexa applied for child users as part of this feature.
      • The implications of this issue were that content that was inappropriate for children along with preferences personalized to adult users in the household – such as shopping, music, movies, reminders, etc. could inadvertently become accessible to children, which compromised user trust and safety standards critical to the Alexa brand. As a result, the issue was escalated to a high-priority case requiring immediate resolution to mitigate further negative user experiences.
    • Task:
      • My responsibility was to come up with a mitigation strategy to urgently root-cause the issue and come up with a resolution plan.
      • However, since this was a new feature, there was no precedent or clear plan for handling such a unique issue, such as simply reverting the model to a prior version. This increased the complexity of decision-making and required swift problem-solving.
    • Action:
      • Acting on the urgency of the issue, I facilitated a brainstorming session with subject matter experts within my team and across our XFN to chalk out a plan focusing on root-causing the issue at hand, consulting with relevant folks and obtain their perspectives. To avoid negatively impacting customer experience and potential PR issues as a result of it, we decided to temporarily pause the feature until we root-caused the issue and implemented a resolution plan.
      • I set up daily stand-ups with the team, to track progress and align efforts in driving the issue to closure.
      • I involved a data scientist on my team to mine relevant utterances from the Alexa database, and a principal speech engineer to help understand the failure patterns and pinpoint the issue. The issue was a corner case where the model would sometimes go wrong with several sources of multilingual speech in the background, such as TV running in the background, along with a phone conversation or people talking or singing, etc.
      • Partnering with the Alexa recording studio, we quickly gathered new utterances with several multilingual noise sources and had them annotated with the help of our annotation teams, which were used to fine-tune the model. However, due to intense time constraints, we couldn’t afford the standard two-month offline and online validation cycle and had to make swift decisions, relying heavily on our domain expertise and intuition to proceed
      • I worked closely with the principal engineer on my team and devised a streamlined evaluation process, using a small, representative dataset for offline evaluation. This allowed us to quickly test for the gaps and check if we’ve caused any performance regressions.
      • Due to the wider confidence interval margins, we made an informed decision based on our domain knowledge, relying on our best judgment and intuition to move forward. At the same time, we took steps to assess potential risks by analyzing sub-population coverage within the new dataset.
      • In parallel to A/B testing, we conducted dogfooding to assess model behavior internally. We setup A/B tests for the updated model with a gradual rollout starting with 5%, cautiously looking for feedback from the field by setting up real-time monitoring to track performance and mitigate risks.
    • Result:
      • The end result was that the team successfully fixed the issue in a timely manner, addressing our customer’s concerns and improving customer experience.
      • Once the fix was confirmed stable, we rolled out the model update to all customers worldwide.
      • To avoid similar issues in the future, I initiated a dedicated project to revise our evaluation data distribution, using augmentation techniques to improve coverage for future scenarios.
    • Reflection:
      • In hindsight/retrospect, this experience taught me the importance of (i) developing a thorough understanding of the situation and (ii) carving out a resolution path that accommodates existing constraints, allowing for swift and informed decision-making in the presence of ambiguity. Lastly, I’d say when time is critical, relying on intuition and experience while balancing speed and accuracy is critical for success.
  • Story 2:
    • Situation:
      • Recently, the AWS QuickSight service, which serves as our backbone for system logging and monitoring, experienced a service outage. Since we rely heavily on QuickSight for real-time insights into application performance, particularly for identifying failed user interactions, the interruption posed a serious risk to the health of all of our deployed applications. With QuickSight offline, we lost visibility into potential issues, which could have had a significant impact on user experience.
      • The AWS QuickSight team began actively investigating the issue right away, but we had no clear idea of how long the service would be down or what steps to take in the interim to mitigate the risk. A prolonged QuickSight outage can result from infrastructure failures in its underlying services like RDS (for data storage) or EC2(for compute resources), causing cascading effects that can make it difficult to isolate and resolve the root cause. The uncertainty made it difficult to plan our next moves effectively.
    • Task:
      • I needed to ensure continued monitoring of our applications’ health during the QuickSight outage.
      • I also had to prevent user issues from going undetected, despite our primary monitoring tool was unavailable.
    • Action:
      • Without a suitable automated alternative, I led the team to pull raw logs directly from S3 and use Python libraries such as Pandas for data manipulation and Matplotlib for visualization. This involved writing custom scripts to parse and analyze log data, which was time-consuming and required meticulous attention to detail to ensure no issues were missed.
      • We created a shift-based schedule where team members would manually run the Python scripts, review the generated reports, and monitor key performance indicators at regular intervals. This ensured that someone was always overseeing the system, but it was labor-intensive and slowed down other ongoing tasks.
      • I kept in constant touch with the QuickSight team to receive updates on their progress and prepared to transition back to our standard monitoring processes as soon as QuickSight was restored.
      • To streamline the tedious manual monitoring, I developed detailed documentation and guidelines to help team members efficiently run the scripts and interpret the results, reducing the margin for error and improving response times.
    • Result:
      • Despite the lack of an automated tool like QuickSight, the manual monitoring process using Python scripts allowed us to detect and address user issues promptly, preventing any major disruptions to our applications.
      • No significant user-impacting issues went undetected during the outage, maintaining a high level of user satisfaction and trust in our services.
      • The QuickSight team resolved the service interruption within 12 hours, allowing us to resume normal monitoring operations without any lasting issues.
      • The experience highlighted the importance of having backup plans and increased our team’s ability to handle similar situations in the future, albeit through more laborious methods.
    • Reflection:
      • Learned the critical need to have backup monitoring strategies, even if they are less efficient, to ensure continued oversight during service disruptions.
      • Recognized the value of being flexible and resourceful when faced with unexpected challenges and the absence of clear solutions.
      • Although tedious, developing structured manual monitoring procedures proved essential in maintaining application health and taught me how to streamline processes under pressure.
      • The experience reinforced the importance of maintaining open lines of communication with service providers to stay informed and ready to adapt as the situation evolves.

How can a service such as QuickSight experience a prolonged outage?

  • A service like AWS QuickSight can experience a prolonged outage due to several factors, including infrastructure failures in its underlying components, such as Amazon RDS (for data storage) or EC2 (for compute resources). If these services face disruptions, it can cause cascading effects, impacting QuickSight’s ability to function. Network connectivity issues within AWS data centers or regional outages could further extend downtime. Additionally, software bugs or misconfigurations during updates or scaling efforts might prevent the service from recovering quickly. Lastly, delays in diagnosing complex interdependent failures across AWS services can contribute to prolonged outages.

Tell me about a time when you had to manage competing priorities in order to deliver on a project.

  • Story 1 (Multimodal Model x Self - Self Model Compression):
    • Situation:
      • At Amazon Alexa, where I supported query understanding and personalization, we were leading a critical project to release a multimodal transcription model for the Echo Show, an Alexa device with a built-in display with a camera. The goal was to address performance gaps in query understanding seen with the prior speech-only transcription model while transcribing commands in a noisy environment—for instance, in a busy kitchen with multiple sources of background noise—by integrating both audio and visual signals for more accurate speech transcription. To set some context, query understanding is one of the initial steps in Alexa’s flow, since it maps user commands to a set of over 200 intent APIs and their arguments that control all of the device’s use-cases, from shopping to playing music to setting reminders to controlling smart home devices. However, given that query understanding was a gate for downstream models in Alexa’s flow, errors at this stage can easily snowball/propagate/cascade further to models further on in the query’s lifecycle/flow. As a result, this initiative was strategically important as it directly impacted user experience and retention.
      • While our offline evaluation showed that the multimodal model was able to successfully address the performance gaps compared to the speech-only model, given that this was the first time we were experimenting with multimodal transcription coming from speech-only transcription, we realized that the latency projections that we used for model planning were inaccurate.
      • This led to our latency numbers being ~12% off compared to our initial projections, which exceeded our latency requirements. The challenge here was that these were strict to ensure seamless, real-time user interactions with the device – missing these targets would lead to users waiting inordinately long to get a response back from Alexa, leading to a poor user experience. This led to competing priorities, which required us to intricately balance performance to offer improved query understanding while ensuring we stay within our latency limits in favor of user engagement and satisfaction.
    • Task:
      • My next step was to come up with a plan to satisfy both objectives: improving the performance of query understanding by deploying a multimodal model, while simultaneously addressing the latency concerns to ensure an optimal user experience.
    • Action:
      • I worked closely with my team to chalk out a plan to find a point on the performance vs. latency graph that balanced the two priorities. We decided to explore model compression techniques to reduce latency while maintaining as much performance as possible.
      • Using the RICE framework for prioritization, I worked with my PM to re-do sprint planning by identifying tasks that could be deferred to future sprints and low-priority tasks that could be moved to our backlog, to accommodate the additional effort required. This approach allowed us to focus on implementing model compression techniques—such as pruning, knowledge distillation, and quantization—similar to those used in training smaller models like Llama 3.2 1B and 3B, to reduce the model’s footprint with minimal performance trade-off.
    • Result:
      • The end result was that we identified a middle ground that satisfied both constraints – addressing the performance gaps within the latency constraints. While the model took a minor performance hit, it was still a significant improvement over the previous version, and the latency was kept within acceptable thresholds.
    • Reflection:
      • Looking back, this experience highlighted the importance of finding a middle ground to transform a challenging situation into a win-win. It also highlighted/underscored/reinforced the need of effective communication and empathy as tools to balance competing priorities—such as performance and latency—and build and maintain strong working relationships.
  • Story 2 (Let’s Chat x TTS - Self Model Compression):

    • Situation:
      • At Amazon Alexa, where I supported the query understanding and personalization team, Alexa’s “Let’s Chat” mode was one of our flagship projects. To set some context, Alexa’s Let’s Chat mode is powered by an LLM and allows users to engage in open-ended, natural conversations with Alexa. The vision for this feature was to broaden the range of topics Alexa could handle, offering more intuitive and context-aware responses, with the ultimate goal of improving customer engagement as a key business metric. While offline testing revealed great benchmark performance, feedback during dogfooding was that the model’s performance for certain low-resource languages such as Farsi, Tamil, and Malayalam. Digging into some of these samples, we noticed that the model exhibited hallucinations which were evident based on signals such as when users interrupted Alexa mid-way through a response, explicitly pointed out errors in her responses, or asked follow-up questions like “are you sure?”.
      • I worked closely with the Titan team (Titan is Amazon’s foundation LLM, similar to Meta’s Llama) and spearheaded an initiative for addressing these gaps and enhancing the performance of this feature. The primary issue was that the existing model was not grounded in a sufficiently large corpus of customer dialogues encompassing a variety of intents, languages, and use-cases.
      • I proposed deploying a model which was fine-tuned on a more diverse and expansive dataset that covered a broader range of low-resource languages across various real-world use cases and dialogue contexts. However, the caveat was this model was a 12B parameter model, 50% larger than the current 8B model. This model was a significant upgrade in performance from the existing 8B model, based on initial tests conducted by my team which showed substantial improvements in accuracy and a reduction in hallucinations. However, the challenge was that while the 8B model met the latency requirements, the 12B model was about 7% beyond the maximal permissible latency. Since the LLM’s output would be passed as input to a text-to-speech (TTS) model for generating the final speech response, I encountered pushback from the TTS team since this additional latency would eat into their latency budget, which would impact the overall user experience. This brought our launch to a standstill, requiring immediate resolution to move forward.
    • Task:
      • My next step was to come up with a plan to satisfy both objectives: improving the performance of Alexa’s Let’s Chat mode by deploying a larger model trained on more diverse data, while simultaneously addressing the latency concerns to ensure an optimal user experience.
    • Action:
      • I worked closely with my team to chalk outfind a point on the performance vs. latency graph that worked was acceptable to both teams. As part of this discussion, I communicated the importance of accuracy for the end-user experience by showing examples of critical failure scenarios, while also acknowledging their concerns about latency. To figure out an acceptable trade-off between latency and performance, I proposed that we explore model compression techniques to reduce latency while maintaining as much performance as possible.
      • My team took upon the task of applying model compression techniques like pruning, knowledge distillation, and quantization (similar to how smaller Llama 3.2 1B and 3B models are trained), to be able to reduce the model’s footprint without sacrificing too much performance. To meet our planned deadlines, my team re-did sprint planning for the next two sprints and deprioritized some of our lower priority tasks to accommodate the effort involved here.
    • Result:
      • The end result was that we identified a middle ground that satisfied both constraints – addressing the performance gaps within the latency constraints. While the model took a minor performance hit, it was still a significant improvement over the previous version, and the latency was kept within acceptable thresholds.
    • Reflection:
      • Looking back, this experience highlighted the importance of finding a middle ground to transform a challenging situation into a win-win. It also highlighted/underscored/reinforced the need of effective communication and empathy as tools to balance competing priorities—such as performance and latency—and build and maintain strong working relationships.
  • Story 3 (GenAI Infrastructure x Rufus):
    • Situation:
      • I was leading a project to build a centralized end-to-end infrastructure to manage and keep the Retrieval-Augmented Generation (RAG) databases up to date. The goal was to centralize this infrastructure to reduce duplication of effort across various GenAI projects, including internal projects like customer service chatbots.
      • Simultaneously, I was responsible for delivering Rufus, the Amazon retail chatbot, which relied heavily on the same RAG infrastructure. Rufus needed real-time, up-to-date product information to support customer interactions during the busy holiday shopping season. The holiday deadline was fixed, and missing it could negatively impact sales and customer experience. Both the centralized RAG infrastructure and Rufus relied on the same technical resources, creating a bottleneck that required careful planning and coordination to resolve.
    • Task:
      • My task was to balance the competing priorities of building the centralized RAG infrastructure for future scalability while ensuring Rufus was delivered in time for the holiday shopping season. I needed to ensure the infrastructure was developed efficiently without delaying Rufus and manage the limited resources shared by both projects.
      • I had to effectively allocate resources, ensuring engineers working on both projects were not overburdened while still meeting deadlines. This involved finding synergies between the projects to avoid redundant work and maximize efficiency.
    • Action:
      • I applied the RICE framework (Reach, Impact, Confidence, and Effort) to determine how to prioritize tasks. Rufus, being a customer-facing project with a fixed holiday deadline, had a higher Impact and Reach score, making it the immediate priority. However, the centralized RAG infrastructure had a high Confidence score due to its long-term importance for multiple GenAI projects.
      • I identified specific areas where work on the RAG infrastructure could be overlapped with Rufus. For instance, engineers working on the data ingestion pipelines for the RAG system could simultaneously ensure that Rufus’ data retrieval and product catalog access were fully functional. This allowed the infrastructure work to continue while providing Rufus with real-time product updates needed for customer interactions.
      • I conducted a thorough capacity assessment of our engineers to understand how much bandwidth each had. Based on that, I distributed tasks that would benefit both projects, such as optimizing data retrieval and indexing, to avoid duplicating efforts and reduce the workload for engineers working on both the infrastructure and Rufus.
      • I organized regular check-ins with stakeholders and the team to track progress on both projects, adjust priorities as needed, and resolve any bottlenecks early. This ensured that engineers could maintain focus and deliver both projects on time without being overwhelmed.
    • Result:
      • The end result was that by leveraging the overlap between the two projects, we successfully delivered Rufus on time for the holiday season, ensuring it had real-time, accurate product data for customer interactions. Simultaneously, we completed the foundational RAG infrastructure, which is now central to other GenAI applications, ensuring scalability and up-to-date retrieval capabilities for multiple future projects.
    • Reflection:
      • Looking back, this experience taught me the value of viewing challenges as opportunities for success. finding synergies between competing projects and aligning priorities using the RICE framework, we avoided bottlenecks and delivered both projects successfully.

Describe a time when you had to quickly learn something new and complex in order to ramp up for a project or solve a problem.

  • When I moved to Siri in Apple, a big part of it was speech which I didn’t have experience in.
  • I had taken NLP, ML and had experience with those.
  • Ramping up with ASR and other speech jargon, had to do it very quickly.
  • The product I created with Siri will be launching soon in collaboration with the VR headset.

  • Situation:
    • When I transitioned to working on Siri at Apple back in 2017, I was faced with a new challenge—working with speech technology, specifically Automatic Speech Recognition (ASR) and other speech-related systems. While I had a strong background in NLP and machine learning, speech technology was outside my previous experience, and I needed to quickly ramp up to contribute effectively.
  • Task:
    • My task was to quickly learn the intricacies of ASR and related speech technology concepts to successfully deliver a Siri-like voice assistant but specifically targeted towards Apple’s Vision Pro headset. I needed to gain a deep understanding of the technology in a short period to ensure the product’s success and meet the project timelines.
  • Action:
    • I immersed myself in learning the necessary speech technology concepts by coming up with a plan that consisted of both theoretical and practical components.
    • To build a foundational understanding of ASR, TTS, and the specific jargon used in the field, I reached out to subject matter experts (SMEs) such as principal engineers in speech technology and sought the right resources by compiling a list of relevant research papers, textbooks, YouTube videos, online resources, etc.. Diving into these materials helped me better grasp the lay-of-the-land and complexities of the domain.
    • To go beyond theoretical understanding and acquire practical knowledge, I worked closely with experienced engineers on the team to get hands-on exposure by running various speech models—adjusting parameters and testing different configurations to deepen my understanding of how these models functioned.
    • I synthesized everything I learned into a speech AI primer, which I published on my blog at speech.aman.ai. This primer captured key insights on ASR, TTS, wakeword detection, etc., serving as a resource not only for the team but also for anyone beginning their journey in speech technology.
    • I applied my knowledge of NLP and machine learning to draw parallels with speech technology, which helped me contextualize the new information and integrate it quickly into my work.
  • Result:
    • Within a short period, I successfully ramped up on ASR and speech technology, contributing to the development of a Siri-like voice assistant that was launched at Apple’s Vision Pro announcement.
    • Chalking out a structured approach to enable rapid learning both from the theoretical as well as practical lens allowed me to contribute meaningfully to the project, ensuring that the speech engine were well-integrated with the product’s broader functionality.
  • Reflection:
    • In hindsight, this experience taught me the value of proactively identifying resources, collaborating with experts, and focusing on both theoretical and hands-on learning, especially when facing a steep learning curve.

Tell me about a time when you needed to overcome a barrier in your work to achieve an end result.

  • Story 1:
    • Situation
      • At Amazon Alexa, where I supported query understanding and personalization, I was leading a project to develop the next generation of the semantic intent recognition for Alexa, powered by quantized LLMs. To set some context, intent recognition is a crucial component in creating a seamless user experience since it maps user commands to a set of over 200 intent APIs and their arguments that control all of the device’s use-cases, from shopping to playing music to setting reminders to controlling smart home devices. The new model represented a significant shift from the existing implementation, which relied on a combination of shallow models and rule-based methods. The idea behind the project was to enhance Alexa’s intent parsing ability across multiple devices, languages, and a wide range of contextual variations, improving Alexa’s capacity to accurately interpret user commands on devices worldwide.
      • The QA team was responsible for ensuring that the system could accurately recognize and respond to user commands across all these variables. Given the complexity of the feature, comprehensive testing was critical to validate its accuracy, adaptability, and robustness. This required testing both automated scenarios and manual, multi-intent interactions to ensure the feature worked seamlessly in real-world environments. Without this thorough testing, there was a risk of failure in key use-cases, impacting the overall user experience.
      • Unfortunately, just as we were entering this critical testing phase, the QA team experienced a major setback. They had unfortunately lost two of their most senior members within a week—one left the company, and another had to take an unexpected medical emergency leave. This left the team severely understaffed to carry out the necessary testing to certify this project for launch. This situation blocked progress in the final lap of the project, which in turn, jeopardized the project’s launch timeline.
    • Task
      • To address this, I had to come up with a mitigation plan to ensure that the comprehensive testing of our new Alexa feature—across the matrix of multiple devices crossed with languages crossed with intents—was completed on schedule despite the QA team’s staffing issues.
    • Action
      • I immediately reached out to the QA team’s manager to assess their remaining capacity and re-prioritize our testing plan from earlier to distill the most critical aspects of the testing plan that we should focus on. I proposed a middle ground by extending some of my team’s bandwidth and working with their team to tackle the testing tasks for this project.
      • Using the RICE framework for prioritization, I worked with my PM to re-do sprint planning by identifying tasks that could be deferred to future sprints and low-priority tasks that could be moved to our backlog, to accommodate the additional effort required.
      • To ramp up my team members, I requested the QA manager to arrange knowledge-sharing sessions led by their team members to provide insights on their testing infrastructure and automation frameworks. This allowed us to distribute some of the testing load and have my team to contribute to the testing plan.
      • Simultaneously, I coordinated with senior leadership to bring in resources from a different org’s QA team to support the project, focusing on the more complex multi-intent scenarios that required manual attention.
    • Result
      • Using a combination of re-prioritizing tasks, cross-training my team, and utilizing external QA members, we ensured that the new semantic intent recognition system for Alexa was fully vetted before launch.
      • As a result of this strategy, we were able to keep the project on track without significantly compromising the quality of the product.
    • Reflection
      • This experience reinforced the importance of flexibility and finding a middle ground (or agility or nimbleness or adaptability) to achieve win-win situations, especially in the face of a significant challenge.
  • Story 2:
    • I am a very technical leader.
    • Barrier: person, the PM.
    • Barrier: technical.
    • Dependency on XF teams.
    • The barrier was that the timelines didn’t intersect with ours, sunsetting the current infrastructure, new infrastructure didn’t align with the timeline.

    • Situation:
      • As a technical leader, I was managing a project that depended heavily on XF teams to deliver an end result.
      • I faced multiple barriers along the way—both technical and interpersonal.
      • One of the significant challenges was a misalignment in timelines between our team and a XF team responsible for sunsetting a legacy infrastructure (AWS Data Pipeline) and deploying new infrastructure.
      • The PM from the other team did not prioritize our needs, which created further complications for our progress.
    • Task:
      • My task was to overcome the dependency on the XF team’s infrastructure timeline while ensuring that our project remained on track.
      • We needed to find a way to either expedite their process or adjust our approach to ensure we could still meet our deadlines without compromising the project’s quality.
    • Action:
      • I initiated direct communication with the PM of the XF team to better understand their challenges and explain the impact of their timeline on our project.
      • I emphasized the importance of aligning our efforts to ensure that the transition to the new infrastructure wouldn’t derail our progress.
      • To mitigate the timeline mismatch, I proposed a phased integration plan. This allowed us to continue working with portions of the legacy infrastructure while gradually incorporating the new infrastructure as it became available.
      • I also coordinated closely with my team to ensure that we could adapt to changes in the XF team’s progress without losing momentum on our own work.
      • Additionally, I organized weekly syncs between our teams to improve transparency and alignment. This helped identify any new blockers early on and allowed us to address them before they became critical issues.
    • Result:
      • Through proactive communication and collaboration, we were able to work around the XF team’s timeline and successfully integrate the new infrastructure in phases.
      • This approach ensured that our project remained on track, even though the technical dependency posed a significant barrier.
      • We successfully met our deadlines and delivered a high-quality end result without having to wait for the full infrastructure transition.
    • Reflection:
      • This experience reinforced the importance of clear communication, negotiation, and adaptability when facing barriers—especially when those barriers involve external dependencies.
      • By remaining solution-oriented and flexible, I was able to help both teams align on a path forward, ultimately ensuring the project’s success despite initial roadblocks.

Tell me about a time when you faced a significant setback that forced you to reprioritize your work.

  • Story 1 (Within a project):
    • Situation:
      • At Amazon Alexa, where I supported the query understanding and personalization team, Alexa’s “Let’s Chat” mode was one of our flagship projects. To set some context, Alexa’s Let’s Chat mode is powered by an LLM and allows users to engage in open-ended, natural conversations with Alexa. The vision for this feature was to broaden the range of topics Alexa could handle, offering more intuitive and context-aware responses, with the ultimate goal of improving customer engagement as a key business metric.
      • However, during this project, Alexa went through a significant re-org which impacted our team. This resulted in the loss of two key engineers and scientists, forcing us to rethink our strategy and scaling down capacity significantly.
    • Task:
      • My responsibility was to assess the situation and reprioritize work based on our new limitations while ensuring delivery on key business objectives.
      • I needed to identify which projects were critical to the business and reallocate resources accordingly, keeping the remaining team motivated and aligned on new priorities.
    • Action:
      • Using the RICE framework for prioritization, I worked with my PM to re-do sprint planning by identifying tasks that could be deferred to future sprints and low-priority tasks that could be moved to our backlog, to accommodate the change in resources for this project. This approach allowed us to focus on the most important tasks for the project, without delaying the timelines significantly.
      • I also worked with my team to identify areas where we could automate workflows or use existing tools to reduce manual effort, freeing up engineering time.
      • Once we had our due-diligence done, I shared our new priorities with leadership and stakeholders to ensure they were alignment (i.e., get their buy-in). - - To ensure our execution stayed on track, I worked closely with the PM to weekly sprint planning sessions and daily standups to track progress, reprioritize tasks, and address roadblocks. I regularly communicated with the team, explaining the reasoning behind changes and providing support to keep them focused and engaged despite challenges.
    • Result:
      • By prioritizing the voice shopping project, we successfully delivered the personalized GenAI shopping feature on Alexa, which launched on time and received positive feedback from users and stakeholders.
      • The project contributed to increased user engagement and higher shopping conversion rates during peak shopping periods, such as Black Friday and the holiday season.
      • Although the Smart Home enhancements project was delayed, it remained in the pipeline for future development when resources allowed.
    • Reflection:
      • Looking back, this experience reinforced the importance of structured prioritization using tools such as the RICE framework for clear, data-driven decisions.
      • It also highlighted the need for transparent communication and decisiveness in times of uncertainty, helping the team stay motivated and aligned with goals despite setbacks.
  • Story 2 (Across two projects):
    • Situation:
      • I was leading two major projects within the Alexa team when a significant layoff impacted our workforce.
      • One of the projects was the development of a personalized voice shopping experience on Alexa, involving GenAI integration to tailor product recommendations based on individual user profiles. The second project focused on enhancing Alexa’s interaction with smart home devices using natural language commands.
      • The team restructuring resulted in the loss of several key engineers and scientists, forcing us to rethink our strategy and scale down capacity significantly.
    • Task:
      • My responsibility was to assess the situation and reprioritize work based on our new limitations while ensuring delivery on key business objectives.
      • I needed to identify which projects were critical to the business and reallocate resources accordingly, keeping the remaining team motivated and aligned on new priorities.
    • Action:
      • Using the RICE framework, I assessed the priorities associated with each project. This approach allowed us to focus on looking at our current slate of projects from the lens of RoI.
      • The personalized voice shopping project had a higher impact and strong alignment with Amazon’s long-term vision for Alexa, so it was prioritized. I scored it high in both Impact and Confidence, as foundational work was already completed, and it was a strategic initiative tied to increasing customer engagement and driving conversions.
      • The Smart Home project, while valuable, was deprioritized due to lower immediate business impact and higher engineering resource demands that we could no longer support.
      • I also worked with my team to identify areas where we could automate workflows or use existing tools to reduce manual effort, freeing up engineering time.
      • Once we had our due-diligence done, I shared our new priorities with leadership and stakeholders to ensure they were alignment (i.e., get their buy-in). Once we had agreement, I restructured the team, assigning remaining engineers to the voice shopping project to ensure we met critical deadlines.
      • To ensure our execution stayed on track, I worked closely with the PM to set up weekly sprint planning sessions and daily standups to track progress, reprioritize tasks, and address roadblocks. I regularly communicated with the team, explaining the reasoning behind changes and providing support to keep them focused and engaged despite challenges.
    • Result:
      • By prioritizing the voice shopping project, we successfully delivered the personalized GenAI shopping feature on Alexa, which launched on time and received positive feedback from users and stakeholders.
      • The project contributed to increased user engagement and higher shopping conversion rates during peak shopping periods, such as Black Friday and the holiday season.
      • Although the Smart Home enhancements project was delayed, we took upon it in the next half once additional resources were available.
    • Reflection:
      • Looking back, this experience reinforced the importance of structured prioritization using tools such as the RICE framework for clear, data-driven decisions.
      • It also highlighted the need for transparent communication and decisiveness in times of uncertainty, helping the team stay motivated and aligned with goals despite setbacks.

Tell me about a result you achieved for your team that you are most proud of. / Have you introduced any process changes in your team? / How do you interview and onboard candidates as a manager?

  • Situation:
    • As a manager in the newly formed Generative AI org at AWS, I noticed that there was no consistent structure or process for interviewing or onboarding new team members across the org. This inconsistency was leading to varied candidate evaluations and inefficient onboarding. Additionally, we were planning to scale our team from 2 to 18 people within two quarters, making it critical to have a structured way to onboard people swiftly and effectively.
  • Task:
    • I took the initiative to create a standardized interview guide and a structured onboarding program for new hires, ensuring that our org was equipped with the necessary skills to contribute to projects from the start.
  • Action:
    • After personally sourcing and recruiting each candidate for the team, to ensure we hire the best talent, I created a comprehensive interview guide that standardized the process for evaluating technical skills, cultural fit, and problem-solving abilities. This guide included well-defined criteria for each role, example questions, evaluation rubrics, and feedback forms to ensure consistent and fair assessments across the org.
    • Alongside the interview guide, I also developed detailed onboarding documentation to help new hires integrate more smoothly. This included technical setup, resources, pointers to trainings, etc.
    • To ensure a structured onbording for new hires, I designed a onboarding plan that consisting of a theoretical component and a practical component.
    • The theoretical component ensured that new engineers had the necessary theoretical background in machine learning (ML). I provided them with my ML fundamental primers (publicly available on aman.ai), covering essential topics like learning paradigms, training neural networks, basics of NLP+Vision+Recommender Systems, and model evaluation. I also curated additional research papers, online courses, and tutorials to deepen their ML knowledge.- To build strong practical know-how and obtain real-world exposure, new hires transitioned to hands-on roles, supporting projects for a couple of weeks and eventually began leading projects. This blend of theory and practice helped ensure a smooth landing within the team.
    • The development program included regular check-ins to assess progress and course-correct when necessary. This continuous feedback loop created a strong support system for new hires.
  • Result:
    • This interview guide quickly became the go-to standard across the entire org, with about 20 managers utilizing it to support the scaling of their respective teams, leading to more consistent and objective hiring decisions.
    • The onboarding documentation and structured development program reduced ramp-up time, enabling new hires to onboard effectively and contribute faster.
    • Together, the process changes enabled the successful scaling of the team from 2 to 18 people, contributing to a more efficient, cohesive, and scalable process across the GenAI org.
  • Reflection:
    • In hindsight, this experience underscored the importance of taking initiative to address organizational gaps by proposing consistent frameworks to ensure alignment across the organization.

What was your biggest mistake as a people manager?

  • Story 1:

    • One of the biggest mistakes I made was failing to provide proactive upward feedback to my manager. To set context, my manager recently inherited our org of 80 people after my previous manager’s departure. He was unfamiliar with our team’s specific workflows and processes, and his approach to management was drastically different from what the team was used to. He had a vision of revamping our processes to increase operational efficiency, which was well-intentioned but didn’t account for the unique needs and workflows we had developed over time.
    • For instance, our bi-weekly project updates had been structured to allow for a balance between in-depth status reporting and maintaining the team’s autonomy. This process ensured that team members weren’t bogged down by excessive oversight, allowing them to focus on execution. Additionally, we had streamlined our approval process to prevent bottlenecks, allowing for quicker decision-making during critical project phases. This change was implemented after a previous project experienced a two-week delay due to multiple layers of sign-offs, which resulted in a missed deadline. By streamlining approvals, we avoided these delays on subsequent projects, which significantly improved our ability to move quickly during important phases.
    • Unfortunately, I failed to communicate these nuances to my new manager. I didn’t explain that these workflows had already been optimized based on specific challenges we had encountered and that sudden changes—such as doubling the cadence of our status meetings and additional approval layers—would likely disrupt our momentum, delay decision-making, and diminish the team’s sense of ownership..
    • When the changes were introduced, feedback from both personal 1:1 discussions and anonymous polling of daily connection scores revealed growing frustration over challenges in meeting project deadlines due to the increased focus on status reporting. This led to increased frustration and lowered morale in the team, outcomes that could have been mitigated had I been more proactive in providing upward feedback. Instead, I waited until it became a more significant issue, which resulted in unnecessary friction within the team.
    • Looking back, I learned the importance of being proactive in providing upward feedback, especially when changes could significantly impact team dynamics. Since that experience, I’ve made it a priority to proactively advocate for my team’s needs and ensure that leadership has the relevant context about how decisions may affect the team’s overall productivity and well-being.
  • Take two

    • Took on additional work accidentally
    • other teams in Amazon have a pipeline to productionize
    • This particular team had its own specific pipeline, very unique due to data governance/ data privacy laws they had to follow as this was very sensitive data
    • Could take longer than development

Tell me about your current and/or greatest developmental area. / What are your areas of growth around execution?

  • Developmental Area: While I have successfully advocated for my team’s contributions to parallel teams within our org and XF partners, a key developmental area that I’m currently working on is the ability to identify opportunities to champion my team’s work beyond our org to other business units within the company. Specifically, I aim to identify and champion opportunities to cross-pollinate our work in GenAI to other parts of the company such as Amazon Prime Video, Amazon Music, etc. where it can drive significant value. By proactively promoting our work for broader adoption, I hope to increase our impact beyond our immediate org and across the company.

  • How I Recognized Growth Opportunity: I recognized this growth opportunity through my mentor, who is a director in a parallel org. Observing her in meetings, I saw how she consistently championed her team’s contributions across various teams and departments. A notable example was when her team developed libraries to automate large-scale distributed training, which were adopted company-wide, resulting in improved efficiency for training some of our large models. Witnessing her success inspired me to recognize the importance of proactively advocating for my team’s work.

  • Steps to Improve: Being self-aware of this gap, I have taken a couple of steps to make improvements. I began by developing a clear understanding of my team’s strengths and the potential value we can offer to other teams. Additionally, I proactively explored opportunities by identifying key stakeholders across the organization, attending cross-org leadership syncs and technical deep dives to familiarize myself with their roadmaps at a high level, and even cold-messaging peers to find collaboration opportunities. I’ve worked on my pitching skills to better communicate our work’s impact and sought feedback from my mentor to learn how to more effectively champion my team and identify opportunities.

  • Recent Progress Made: Recently, my team led an initiative to build infrastructure for data pre-processing, fine-tuning, and offline evaluation of LLMs. I proactively championed this initiative in cross-org forums, including technical deep dives and working groups, highlighting how it streamlines the model development lifecycle. I also facilitated knowledge-sharing workshops to demonstrate its benefits, leading to adoption by several teams, improving efficiency and positioning our team as a key contributor of foundational AI capabilities.

  • Impact on Team and Career: With a growth mindset, I like to reflect on gaps and figure out ways to optimize for win-win scenarios. In this case, by advocating for broader cross-org adoption of our work, not only did the team gain more recognition and influence, but on a personal level, this offered me an opportunity to develop into a more strategic leader, with the ability to influence at a broader organizational level.

L&D questions

Tell me about a project you are most proud of.

  • Most proud of a project that was in a crisis state, but I came in and fixed it via technical and leadership skills.
  • Rule-based system in Alexa for something and fixed it, missing deadlines, failing customer metrics, etc.

  • Situation:
    • One of the projects I am most proud of involved stepping into a critical situation with Alexa’s rule-based FAQ system for customer support. This system was designed to provide quick answers to common customer queries, but it was underperforming.
    • Customers were receiving irrelevant or incorrect responses, leading to frustration and a drop in customer satisfaction scores. The project was in jeopardy, and immediate action was needed to prevent further negative impact.
  • Task:
    • My task was to diagnose and resolve the issues within the rule-based FAQ system to improve its accuracy and reliability.
    • This required technical expertise to identify flaws in the rule logic and leadership to coordinate a swift and effective response from the team.
  • Action:
    • I began by conducting a thorough audit of the existing rules and logic in the FAQ system. I discovered that many rules were outdated, overly broad, or conflicting with one another, which caused the system to misinterpret customer queries.
    • Working closely with my team, we updated and refined the rule set to be more specific and aligned with current customer needs. We eliminated redundant and conflicting rules and introduced new ones based on recent customer feedback and query data.
    • To enhance the system further, I proposed integrating a lightweight machine learning component to better handle natural language variations and improve the system’s ability to understand and respond to a wider range of customer inquiries.
    • We also established a continuous monitoring process to regularly update the rule set and machine learning models based on ongoing customer interactions.
  • Result:
    • After implementing these changes, the accuracy of the FAQ system improved significantly. Customers began receiving relevant and helpful responses, which led to an increase in customer satisfaction scores.
    • The number of escalations to human support agents decreased, reducing operational costs and allowing support staff to focus on more complex issues.
    • The project was brought back on track, and the improvements had a lasting positive impact on the overall customer experience with Alexa.
  • Reflection:
    • This project underscored the importance of combining technical problem-solving with effective team leadership to turn around a struggling project, ultimately delivering significant value to both the customers and the organization.

Describe a time when you started collaboration across teams.

Situation:

  • I led a project within Amazon Alexa focused on enhancing the conversational mode feature, specifically the “Alexa, let’s chat” experience. This allowed users to engage in more natural, open-ended conversations with Alexa, including queries like, “What should I pack?” or “What should I look forward to?”
  • This new conversational capability extended beyond just simple commands and required input from multiple areas, such as retail, web lookups, and reminders.

Task:

  • To make this experience successful, I needed to initiate and foster collaboration across multiple teams, including the retail, web services, and reminders teams, as well as teams working on natural language processing (NLP) and machine learning (ML).
  • The goal was to integrate the diverse functionalities Alexa needed to handle such complex conversational queries effectively.

Action:

  • I started by reaching out to the relevant teams—retail, web services, reminders, and NLP/ML teams—to ensure alignment on the project’s goals. I organized XF meetings to establish how each team’s technology and expertise could be integrated into the “Alexa, let’s chat” mode.
  • We used the Agentic framework to ensure that Alexa could understand context, handle multi-turn conversations, and manage different domains such as shopping queries, scheduling reminders, and pulling information from the web.
  • I worked closely with each team to define their roles in building a seamless user experience and established clear deliverables for each group.
  • I facilitated regular syncs between the teams, ensuring that everyone was aligned on timelines and that any blockers were quickly addressed. This collaborative approach allowed us to leverage each team’s unique expertise to build a more intelligent and fluid conversational experience for Alexa users.

Result:

  • The collaboration across teams led to the successful launch of the enhanced conversational mode in Alexa, allowing users to ask more complex, multi-domain questions and receive helpful, contextually aware responses.
  • The integration of shopping, web lookups, and reminders created a more dynamic and interactive user experience.
  • This project also set a new precedent for cross-team collaboration within Alexa, demonstrating that when teams across domains work together effectively, we can push the boundaries of what conversational AI can achieve.

Reflection:

  • This experience reinforced the importance of fostering collaboration across teams, especially in a complex project involving multiple domains.
  • By bringing together different areas of expertise and aligning them toward a common goal, we were able to deliver a highly successful feature that expanded Alexa’s conversational capabilities.

How do you maintain the team’s project priorities and help resolve priority conflicts?

  • Situation:
    • In my role a leader supporting XF projects, last-minute requests or scope changes often crop up requiring swift action so as to not derail ongoing work.
  • Task:
    • During these situations, I like to ensure clear project prioritization, effectively resolve conflicts stemming from competing demands, and maintain the team’s alignment with key business objectives while minimizing disruption.
  • Action:
    • I utilize the RICE framework to evaluate tasks by Reach, Impact, Confidence, and Effort. This helps the team to objectively assess which projects have the highest value, ensuring resources are allocated to the most impactful initiatives.
    • Once priorities are set, I ensure that large projects are broken down into smaller, manageable deliverables. This allows the team to make consistent progress even in cases where project scopes evolve mid-way.
    • To address conflicts, I set up weekly priority syncs where the team, stakeholders, and product managers discuss any misalignments or bottlenecks in real-time. In these meetings, we collaboratively reassess the RICE scores if new information emerges or if external demands shift.
    • I use tools like Kanban boards and Gantt charts to visually represent project timelines, task dependencies, and resource allocation. This helps provide the team with a clear view of where their efforts are needed most and allows me to identify potential areas of conflict early.
    • When conflicts arise across teams, I arrange XF discussions with other leaders to negotiate timelines and resource-sharing, ensuring the highest priority business needs are met without overburdening any one team.
    • To maintain transparency, I conduct monthly project retrospectives, inviting the team to openly discuss any unresolved priority concerns and suggest process improvements. This not only helps in surfacing potential conflicts early but also fosters a culture of continuous feedback and agility.
  • Result:
    • The end result of both (i) leveraging a structured set of tools such as RICE, Gantt charts, Kanban boards, etc. and (ii) promoting proactive communication through syncs and retrospectives, is that the team has been agile and flexible to consistently deliver high-priority projects on time, despite shifting requirements or last-minute changes.

Tell me about a time when you convinced another team to modify its roadmap.

  • Situation:
    • Our team was working on a critical Alexa feature that required infrastructure updates from a XF AWS Infra team, but their roadmap had deprioritized our needs due to a loss of headcount. They had unfortunately lost two of their most senior members within a week—one left the company, and another had to take an unexpected medical emergency leave), resulting in a packed slate of priorities. The XF team was heads-down focused on migrating legacy systems and implementing security enhancements, viewing our needs as lower impact.
    • This led to significant delays for us that risked impacting our overall project timeline.
  • Task:
    • My task was to persuade the XF team to realign their roadmap to prioritize the necessary infrastructure updates for our project, which was essential to prevent further setbacks and maintain alignment with our launch schedule.
  • Action:
    • I initiated a 1:1 conversation with the manager to understand their existing commitments and constraints, as well as to articulate how the delay in their updates was hindering a strategic feature for Alexa, which was tied to key business objectives.
    • I presented a detailed impact analysis, demonstrating how the delay would affect key milestones for our feature launch, highlighting potential revenue loss and missed engagement targets during peak seasons if the project wasn’t delivered on time. This data helped show the urgency from a business perspective.
    • I shaped my narrative drawing parallels to how the XF team’s contribution directly aligned with company-wide strategic goals. This helped connect the dots better. I illustrated how this alignment could increase their visibility and success in delivering critical infrastructure for high-profile Alexa features.
    • To address their concerns about capacity and bandwidth, I proposed a phased integration approach, where we could begin using portions of their infrastructure in incremental stages before the full implementation was ready. This led us to meet-in-the-middle by allowing us to unblock our project while reducing the strain on the infrastructure team’s resources.
  • Result:
    • After these discussions, the XF team agreed to adjust their roadmap, and we implemented the phased integration successfully. This allowed us to continue progressing with key development work without further delays, while the XF team delivered their infrastructure updates over time. Ultimately, both teams achieved their objectives without sacrificing critical milestones.
  • Reflection:
    • This experience taught me that persistence and clear communication are essential when working to resolve misalignments across teams.
    • Offering compromise solutions, such as phased implementation, can help balance competing priorities and ensure both teams meet their goals, fostering stronger cross-team relationships.

Additional Questions

Tell me about a time when you rallied the team to accomplish a project or deliverable that others were initially skeptical about.

  • Amazon Music Maestro link here

  • Situation:
    • As part of the Amazon GenAI team, we were approached by our partners in Amazon Music to integrate a new AI playlist generator—later branded as Maestro—into the Music app. The goal was to enhance user engagement by offering a feature that could create personalized playlists from creative prompts, such as emojis or mood-based queries. However, the idea was met with skepticism from some key stakeholders across teams.
  • Task:
    • The team was initially reluctant for a few reasons:
      • Technical complexity: Some team members were concerned about the latency challenges of implementing real-time AI-driven playlist generation within the app, especially when handling diverse input like emojis or abstract prompts.
      • Unproven engagement: Other teams, such as product and marketing, questioned whether users would actually adopt such a novel feature and if it would drive meaningful engagement beyond the current playlist and recommendation tools.
      • Resource constraints: With other high-priority projects on the roadmap, there was concern that diverting resources to this untested feature might derail timelines for more predictable initiatives.
  • Action:
    • To rally the team and overcome the skepticism, I took the following steps:
      • Vision Alignment: I worked closely with the Amazon Music team to define the broader business impact. We highlighted how AI-driven personalization could differentiate Amazon Music from competitors like Spotify or Apple Music, aligning the feature with Amazon’s broader goal of making AI an integral part of user experiences across products.
      • Stakeholder Buy-in: I organized a series of workshops and meetings to bring XF teams together, from engineering to marketing. I showcased the technical feasibility by presenting early prototypes and latency benchmarks, assuring the team that we could deliver the functionality within the acceptable response time limits.
      • Pilot Proposal: I proposed starting with a beta rollout to a subset of U.S. users. This way, we could test the feature’s impact on engagement with minimal risk, allowing us to validate assumptions without requiring full-scale deployment.
      • Motivation through Collaboration: I actively encouraged team collaboration by creating a shared sense of ownership. I asked team members for their input on critical decisions, which gave them more stake in the project’s success. I also emphasized the unique, cutting-edge nature of the project, which motivated the team by tapping into their desire to work on impactful, innovative technology.
      • Highlighting Broader Impact: I tied the project back to Amazon’s long-term AI strategy, pointing out that the learnings from Maestro could inform future GenAI initiatives across other Amazon products.
  • Result:
    • The team rallied around the project, and we launched the beta version of Maestro to a subset of users on Amazon Music. Despite the initial skepticism, the feature saw higher-than-expected adoption and engagement rates, particularly with younger demographics who embraced the emoji-based and mood-driven playlists. Post-launch surveys indicated that users found it to be a fun and novel way to engage with music, leading to a 10% increase in playlist creation compared to traditional methods.
    • The success of the beta led to the decision to scale Maestro globally. Furthermore, the insights gained from this project laid the groundwork for using generative AI in other Amazon applications, proving the value of our GenAI initiatives across the company.
  • Reflection:
    • This experience reinforced the importance of:
      • Clear communication of the broader vision: Helping the team understand how their work fits into larger company goals can significantly boost motivation.
      • Risk mitigation through pilots: Starting small and gathering data to validate assumptions can turn skeptics into believers without over-committing resources.
      • XF collaboration: Involving stakeholders early in the process and allowing them to contribute ideas can create collective buy-in and shared ownership, driving project success.

Tell me about a challenging business result you achieved that you are especially proud of.

  • Situation: Developing a Vision and Speech Model for the Echo Show
    • As an AWS Applied Science Manager on the GenAI team, we were tasked with developing a vision and speech model for the Echo Show, a smart device designed to detect a user’s location within a room and dynamically adjust its screen to face them. This project aimed to create a seamless experience for activities such as video calls, streaming, and multitasking. The challenge was that all processing had to be done on-device, in real-time, without sending data to the cloud to protect user privacy and comply with Amazon’s strict data protection guidelines.
  • Task: Creating a Real-Time, Privacy-Focused System
    • We needed to build a system that could accurately track the user’s location in real-time using a combination of vision and speech inputs, while ensuring it worked entirely offline. The system had to be sensitive enough to detect movement but avoid unnecessary screen rotations. Additionally, it needed to operate within the hardware constraints of the Echo Show and comply with privacy and data protection requirements, ensuring no data was stored or processed in the cloud.
  • Action: Cross-Team Collaboration and Optimizing for Performance and Privacy
    • Collaborating across departments: I worked closely with the hardware engineering, computer vision, and AI ethics teams to ensure the model met performance, safety, and privacy requirements. We gathered input from all departments to align the technical and operational aspects of the project.
    • Optimizing the model for on-device use: We used lightweight architectures such as MobileNet to enable real-time performance under the device’s hardware limitations. This required multiple iterations and extensive testing to ensure the model operated efficiently without draining battery power or causing lag.
    • Ensuring privacy by design: I led the creation of a privacy-first framework, ensuring that all data was processed on the device itself, with no data sent to the cloud. This involved working closely with Amazon’s privacy and legal teams to ensure full compliance with data protection regulations.
    • Testing and refinement: We conducted several rounds of usability testing in various lighting and room conditions to ensure that the model accurately tracked users. We also tested edge cases such as multiple users, background noise, and partial user visibility to ensure robustness.
  • Result: Successful Launch and Positive Market Impact
    • The final product was a highly responsive, privacy-compliant Echo Show that could accurately track users and adjust its screen accordingly. The project was completed in time for the holiday season, resulting in a significant increase in Echo Show sales. The feature was widely praised for its user-centric design, setting the Echo Show apart from its competitors in the smart home market.
  • Reflection: Key Learnings from the Experience
    • This project reinforced the importance of cross-team collaboration and the need to optimize for both performance and privacy when developing on-device AI models. In future projects, I would focus on earlier identification of potential hardware limitations and work more closely with hardware teams from the outset to prevent issues like power consumption. I would also implement a modular testing approach earlier in the development cycle to address performance issues more proactively.

Tell me about a time you learned from a peer or colleague who had a different working style / skill set

  • Situation: Learning Audience-Focused Communication as an AWS Applied Science Manager
    • XF project communication: As an AWS Applied Science Manager, I worked closely with a peer from the product management team on a large-scale XF project. The project involved both technical and non-technical stakeholders, including engineering teams, business executives, and external partners.
    • Observing colleague’s skills: During our collaboration, I noticed that this colleague had a unique ability to adapt their communication to the specific audience they were addressing. This was critical as the success of our project depended on clear communication across multiple layers of the org.
    • AIM framework introduction: My colleague introduced me to the AIM frameworkAudience, Intent, Message—which provided a structured way to ensure that each communication was not only tailored to the audience but also aligned with the purpose and desired outcome.
  • Task: Learning and Adapting a Structured Messaging Approach
    • Improving communication skills: My goal was to learn how to improve my communication skills, particularly in making my messaging more audience-centric.
    • Adapting to audience needs: While I had always been able to switch between detailed technical discussions and strategic overviews, I realized that I needed to refine how I delivered these messages based on the audience’s priorities and comprehension levels.
    • Using AIM as a tool: My task was to observe and adopt this more structured, audience-tailored messaging approach, using the AIM framework as a guiding tool.
  • Action: Observing, Adapting, and Implementing the Approach
    • Observation of peer’s style: I began by observing how my peer tailored their communication for different audiences, including how they utilized the AIM framework to structure their messages.
    • Applying AIM framework: I applied these insights by using the AIM framework to tailor my own communication. For technical audiences, I incorporated more data-driven insights and granular details. When addressing senior leadership or business teams, I focused on strategic outcomes and high-level summaries.
    • Feedback and iteration: I actively sought feedback from peers and stakeholders on how well my messages resonated. This allowed me to iterate and refine my approach, ensuring alignment between audience, intent, and message clarity, guided by the AIM framework.
  • Result: Improved Communication Effectiveness and Leadership Presence
    • Improved communication efficiency: By implementing a more audience-tailored messaging strategy through the AIM framework, I significantly improved the clarity and efficiency of my communication. This resulted in fewer follow-up questions and better alignment across teams.
    • Enhanced leadership presence: My ability to seamlessly shift between technical deep dives and strategic overviews without losing the audience’s engagement improved my leadership presence. It contributed to faster decision-making, stronger collaboration, and better project outcomes.
  • Reflection: Importance of Audience-Centric Communication
    • Audience-centric communication lessons: This experience taught me the value of audience-centric communication and the importance of using structured tools like the AIM framework.
    • Tailoring content to needs: I learned that successful communication isn’t just about switching between technical and non-technical language but about tailoring the content to the specific needs of each audience.
    • Continuous improvement: Moving forward, I will continue to apply this approach, ensuring that my communication is always relevant, clear, and impactful, guided by the AIM framework.

Tell me about a time that a peer or manager gave you very difficult feedback.

  • Situation: Feedback on Speaking Up More During Presentations
    • Manager’s feedback on communication: A piece of difficult feedback I received from my manager was that I needed to speak up more during my team’s presentations. I believed my reports should get full credit for their work, so I often let them handle the entire demo.
    • Manager’s suggestion to set the stage: My manager suggested I start by setting the stage—offering a quick introduction and explaining the project’s context, especially for non-technical stakeholders.
    • Initial response and actions taken: I took this feedback and began providing brief overviews at the start of presentations to frame the conversation. This allowed the audience to better understand the project, while still giving my team the spotlight.
    • Resulting improvement: It improved the clarity of our demos and helped me grow as a leader, balancing team empowerment with clear communication.
    • Why it was difficult: This feedback was difficult because it was directly impacting my growth, pushing me to step out of my comfort zone.
  • Task: Providing Difficult Feedback to a New Engineer on Scope Management
    • New engineer struggles with scope creep: The most difficult feedback I gave was to a new engineer on my team who was working on a XF project. During the project, the engineer needed to get PR reviews from other teams, and those teams kept requesting additional features. The engineer kept saying yes, leading to scope creep and delaying the release.
    • Feedback on learning to say no: I had to give him feedback on learning how to say no respectfully and manage expectations to protect the project timeline.
  • Action: Conducting the Feedback Discussion and Providing Guidance
    • Engineer’s initial reaction and addressing concerns: During our discussion, the engineer was initially surprised and hesitant. He believed that accommodating all requests was the right thing to do to keep everyone happy and be a good team player. I empathized, understanding that it’s common for new engineers, especially in XF teams, to feel the need to say yes to everything. However, I emphasized that while collaboration is important, it’s equally critical to guard against scope creep to ensure the project stays on track.
    • Providing actionable guidance to manage scope: I offered specific strategies to help him manage these situations:
      • Set clear boundaries during PR reviews, focusing on the original project scope and priorities.
      • Politely but firmly push back on additional requests, using data or timelines to explain why certain features couldn’t be included in the current release.
      • Escalate to me or senior team members if unsure how to handle complex conversations, ensuring he had support when necessary.
  • Result: Engineer’s Improved Scope Management and Proactive Communication
    • Significant improvement in managing scope: After the feedback, the engineer improved significantly in managing his scope. In subsequent meetings, he began saying no when necessary, using the guidance I provided.
    • Increased confidence in pushing back: He also communicated more proactively with me when faced with challenging requests, which helped him feel more confident in pushing back.
    • Resulting timely project deliveries: Over time, his ability to manage scope contributed to more timely deliveries on his projects.
  • Reflection: Key Learnings from Giving Feedback on Boundaries
    • Importance of feedback on boundary-setting: From this experience, I learned that providing feedback on boundary-setting and saying no is as important as technical feedback.
    • Guiding new engineers on balance: It taught me that new engineers often need guidance on how to navigate the balance between collaboration and maintaining focus, and that empowering them with the right tools can improve their performance and confidence in the long run.

Tell me about a time where you disagreed with a colleague and your original stance was wrong.

  • Situation: Disagreement on Whether to Sunset or Refine a Model for Alexa
    • Initial project background: In my last team supporting query understanding and personalization for Alexa, we had developed a V0 version of a model for Alexa that aimed to send personalized product notifications to users. This version was dogfooded internally, and the feedback indicated that the notifications were interruptive and not particularly helpful. As a result, we began discussing whether to sunset the project.
    • My initial viewpoint: I believed that the model’s interruptive nature was the core issue, and that even with better personalization, it wouldn’t be enough to change the overall user experience. The data from V0 seemed to support this, showing limited user engagement and negative feedback. I felt it was best to move on to other projects that seemed more promising.
  • Task: Aligning with the Product Manager’s Vision to Proceed with a V1 Version
    • Conflicting perspectives: The product manager (PM) disagreed with me and was in favor of pushing for a V1 version, believing that with more personalization, the notifications would become more relevant and less interruptive to users. The PM’s stance was that refining the model could turn it into something valuable, while I was hesitant to continue allocating resources to what I believed was an underperforming project.
    • Understanding the opposing view: Through multiple discussions, I learned that the PM was passionate about the potential of the model. They took a data driven approach to show that improving personalization could solve the relevance issue, whereas I remained focused on the data from V0 and was skeptical about personalization being enough to make a significant impact.
    • They had experience with a similar product, within Amazon Prime that started to perform better once the notifications were personalized.
  • Action: Finding a Middle Ground to Move the Project Forward
    • Compromise on resource allocation: Although we couldn’t fully agree on whether to sunset or continue the project, I took steps to find a middle ground. We decided to allocate one machine learning engineer (MLE) to work on the V1 version, focusing on enhancing personalization.
    • Plan for reassessment: We agreed that once the new personalization features were incorporated, we would reassess the project’s progress. This allowed us to move forward without heavily investing resources into what I believed was still a risky project.
    • Resolving the disagreement through compromise: By assigning limited resources, we gave the PM’s vision a chance to prove its value while keeping the team’s overall workload in balance. This cautious approach mitigated risk and allowed us to gather more data before making a final decision.
  • Result: Improved Personalization and Higher User Engagement in V1
    • Positive outcomes from V1: The V1 model delivered significantly better results in terms of personalization, leading to more relevant notifications and improved user engagement.
    • Core issue of interruption partially mitigated: Although the notifications were still somewhat interruptive, the improved content and relevance made them more acceptable to users. The PM’s instinct about personalization was largely correct, and the project showed enough promise to continue iterating.
    • What made me realize I was wrong: The improved engagement metrics from V1 demonstrated that there was potential in personalization that I hadn’t initially recognized. While it didn’t fully resolve the interruption issue, it added value that I had previously overlooked.
  • Reflection: Learning to Balance Data with Colleague Insights
    • Key takeaway from the experience: This experience taught me the importance of balancing data-driven decision-making with the insights and intuition of colleagues who may have a different perspective. Even when early data suggests a project isn’t worth pursuing, there can still be opportunities and learning that can be leveraged from prior projects.
    • Future approach: In the future, I would be more open to exploring incremental improvements rather than relying solely on initial data to make a final decision. Recognizing the potential benefits of continued iteration is essential, especially when a colleague has a strong conviction about a project’s potential success.
    • Being more open to calculated risks: This experience taught me to be more flexible and open-minded, and to consider giving projects a chance to evolve before making a final judgment.

Tell me about a time when you had to align a group of people on an approach when there was disagreement from others.

  • Situation: Disagreement on Whether to Continue Development of a V1 Model
    • Leading a critical project for Alexa notifications: As an AWS Applied Science Manager, I led a project to develop a V1 version of a model for Alexa that delivered personalized product notifications.
    • Initial poor feedback from V0 version: The previous V0 version was internally dogfooded but received poor feedback, with users finding the notifications more interruptive than helpful.
    • Conflicting views on the next steps: Given this negative feedback, I initially believed we should sunset the project and focus on more promising initiatives. However, the product manager (PM) believed that the V1 version, with improved personalization, could address the relevance issue and make the notifications valuable to users.
  • Task: Aligning Multiple Teams with Conflicting Perspectives
    • Need to unify different teams: I needed to align my applied science team, the PM, and key stakeholders from the product and engineering teams on whether to proceed with the V1 version or halt further development.
    • Disagreement among team members: The PM and some product team members supported moving forward with personalization, while many in my applied science team and I were hesitant due to the poor feedback from V0.
  • Action: Addressing Disagreement and Ensuring Alignment Among Stakeholders
    • Understanding and assessing both perspectives: I started by understanding the applied science team’s concerns, which were focused on the interruptive nature of notifications from V0. My team believed that this issue would persist, even with personalization, and wanted to avoid wasting resources. On the other hand, the PM was confident that improving personalization could enhance the user experience and justify further investment in V1.
    • Data-driven communication to influence the group: To bridge the gap, I set up a meeting where I presented the V0 data, emphasizing concerns about the notifications being interruptive. I also showcased the potential risks and benefits of V1, ensuring the team had a full picture of what pursuing V1 could mean. I proposed a compromise, suggesting we allocate minimal resources (one machine learning engineer) to develop V1, giving it a chance without fully committing the entire team, thus mitigating the risk.
    • Acknowledging concerns and proposing a balanced solution: I acknowledged the applied science team’s resource concerns and validated their focus on the V0 data, while also recognizing the PM’s vision for personalization’s potential. We agreed to assign one machine learning engineer to work on V1 with a clear deadline to reassess progress, allowing the PM to pursue their vision while keeping the project scope manageable.
    • Maintaining alignment through clear communication: Throughout the process, I maintained open communication with all stakeholders, providing regular updates on the project’s progress and ensuring everyone understood the reasoning behind the decision to pursue V1 with limited resources. This approach ensured alignment and buy-in from all parties involved.
  • Result: Successful Improvement with Controlled Investment
    • V1 model delivers improved personalization: The V1 model significantly enhanced personalization, leading to better-targeted notifications and higher user engagement.
    • Ongoing challenge with notification interruptions: Although the personalization improvements were successful, the issue of interruptive notifications wasn’t fully resolved.
    • Effective compromise yields success: The decision to proceed with limited investment proved to be a successful strategy, as the project continued to evolve with the improved outcomes from V1.
  • Reflection: Importance of Balancing Data with Vision in Leadership
    • Respecting differing perspectives to find a solution: This experience taught me the value of acknowledging different viewpoints and finding a balanced solution that respects both data-driven concerns and visionary ideas.
    • Clear communication and compromise lead to alignment: It reinforced the importance of clear communication and compromise in aligning teams and making informed decisions that move projects forward.

Tell me about a time you needed to push for a change that you knew would create conflict within the team.

  • Situation: At Amazon, we use a pulse-check system called Connections, which allows me to regularly gauge my team’s sentiments, gather feedback, and understand how they perceive my leadership. This system has been incredibly beneficial for my own growth as a manager, as I’ve always believed that feedback is a gift. I realized that this could be expanded beyond just team-to-manager interactions. I wanted to implement a similar system within my team to gather feedback on intra-team collaboration and cross-team interactions, with the goal of improving communication and productivity. However, I knew this would be met with resistance because some team members might see this as micromanagement or worry about being judged by their peers.

  • Task: My task was to convince the team that this feedback system would enhance collaboration and improve both interpersonal dynamics and project outcomes. I needed to find a way to introduce the system that minimized concerns about over-monitoring and emphasized its value as a tool for professional growth.

  • Action: To ease the transition, I first held individual discussions with team members to understand their concerns and gather initial thoughts about the system. This helped me craft a more thoughtful approach to rolling out the feedback tool. I emphasized that the system’s purpose was to promote transparency, improve our working relationships, and identify areas where team members could support each other more effectively. I also explained that the feedback would be anonymous and constructive, with a focus on development rather than criticism. To address fears of being judged, I implemented a no-blame policy, making it clear that feedback was about improving processes and collaboration, not about assigning fault.

  • Result: While there was initial resistance, the team gradually started to see the benefits of the system as they received positive and actionable feedback from their peers. Over time, the system fostered better communication and stronger collaboration across projects. Team members became more open to constructive criticism, and we saw a noticeable improvement in how XF teams worked together. The system helped to surface minor interpersonal issues before they became larger obstacles, leading to a more harmonious and productive environment.

  • Reflection: This experience reinforced the importance of introducing change thoughtfully and ensuring that the team understands the purpose and benefits of new initiatives. By engaging in open conversations and addressing concerns upfront, I was able to push for a change that initially seemed contentious but ultimately led to stronger team cohesion and improved performance. It also taught me that the way you frame and implement feedback systems is just as important as the feedback itself.

  • Story 2
  • Situation: Inconsistent PR Review Process Leading to Delays
    • Our GenAI team was relatively new and followed Amazon’s coding guidelines, but our pull request (PR) review process lacked consistency.
    • There was often a lack of context in the PRs, making it difficult for reviewers to understand the purpose of the changes and evaluate them properly.
    • Team members would submit code without detailed explanations, leading to inefficient reviews and delayed progress.
    • I proposed a change to standardize our PR process, requiring clear titles, descriptions, and when possible, a demo or evidence of how the code worked.
  • Task: Implement Standardized PR Process to Improve Efficiency
    • The proposed change was to implement a standardized PR process by requiring:
    • A descriptive title.
    • A clear explanation of the problem being solved and how the code resolves it.
    • If possible, a demo or screenshot to provide visual context for the reviewers.
  • Action: Addressing Resistance, Socializing, and Implementing the Change
    • Anticipated resistance and framed conflict constructively: I anticipated pushback due to tight deadlines and concerns that the new process would add extra work. Some team members felt it would slow them down and introduce unnecessary bureaucracy. I welcomed these concerns and framed them as opportunities to improve the process further, encouraging the team to see the value in addressing them.
    • Facilitated discussions and demonstrated value: I held open discussions where the team could express frustrations and provide feedback on the current PR process. I demonstrated how adding context to PRs would reduce back-and-forth communication, improve efficiency, and shared examples from other teams that saw fewer errors with better-documented PRs. This helped the team understand the long-term benefits of the proposed changes.
    • Proposed a small-scale pilot and led by example: To gain buy-in, I suggested a pilot program to test the new process on a few key PRs, allowing the team to experience the benefits firsthand. I also led by example, using the new template in my own PRs, which demonstrated that providing detailed explanations didn’t significantly increase workload.
    • Gathered feedback and iterated: After a few cycles, I actively sought feedback from the team, made minor adjustments to streamline the process further, and continued to emphasize that any disagreements were opportunities to refine the process collaboratively.
  • Result: Improved PR Efficiency and Team Collaboration
    • The team quickly realized the value of the standardized PR process.
    • Reviews became more efficient as reviewers had all the necessary context upfront, leading to faster approvals and reduced follow-up questions.
    • The quality of feedback improved, and the team spent less time resolving misunderstandings, which sped up overall project progress.
    • The process, initially seen as a burden, was fully adopted and led to better collaboration and effectiveness within the team.
  • Reflection: Key Learnings from the Experience
    • In hindsight, this experience reinforced that a conflict or more generally disagreement, when approached constructively, can be a catalyst for enhanced solutions. By framing conflict as an opportunity and welcoming diverse perspectives, I was able to introduce a change that significantly improved both efficiency and team collaboration without causing friction.

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Recruiter - Behavioral

  1. How do you deal with conflict?
    • Provide a structured example, using the STAR method (Situation, Task, Action, Result). Emphasize your ability to listen, mediate, and find a collaborative resolution.
  2. What were some excellent collaborations you’ve had?
    • Share specific examples where teamwork led to impactful outcomes. Highlight XF collaborations and how your efforts contributed to the success of the project.
  3. Can you tell me about four people whose careers you have fundamentally improved?
    • Give examples of mentorship and leadership, discussing how you’ve helped individuals grow through opportunities, guidance, and feedback. Mention tangible results, such as promotions or skill advancements.
  4. Describe a few of your peers at your company and what type of relationship you have with each of them.
    • Talk about a mix of peers with different roles and backgrounds, focusing on the positive dynamics of your relationships. Describe how you collaborate, communicate, and support each other.
  5. What did you do on your very best day at work?
    • Reflect on a day when you felt most productive, fulfilled, and impactful. Discuss a project or milestone that had significant meaning for you and the team.
  6. What does office politics mean to you, and do you see politics as your job?
    • Office politics can be a hindrance to achieving a common goal. I believe the primary focus should always be on delivering cohesive results for our customers. While navigating office dynamics is part of any job, it shouldn’t overshadow the main objective. I aim to build strong relationships and understand different perspectives, using a data-driven approach to minimize any potential political distractions.
  7. Tell me about a project that you led that failed. Why did it fail and what did you learn?
    • Example: I led the “Next Best Action” project aimed at educating users on leveraging the music app. Unfortunately, it came off as a hindrance to users, likely due to the timing and delivery method. I learned that understanding user behavior and preferences early on, as well as testing delivery methods before full implementation, is critical to project success.
  • Behavioral:
    • Career Path:
      • Why did you choose this path in your career?
        • AI is a new field, the sky is the limit, I can make impact
      • Why Meta?
        • Meta is a leader in AI given their research. It’s inspirational the passion and the open-source nature of their work.
    • Navigating the Unknown:
      • How do you navigate undefined or vague requirements?
        • SME’s, Research papers, market research, break it down to its most atomic component
      • How do you handle working without a clear roadmap every day?
        • First identify what success means for this project, work backwards from it
      • How have you delivered in similar situations in your prior roles?
    • Leadership and Influence:
      • How do you pitch ideas to leadership?
        • AIM framework, data driven, POC to show them
      • How do you get buy-in from XF teams (XFN)?
        • Business overall metrics being mutual
      • How have you secured political buy-in in the past?
    • Conflict Resolution:
      • How do you resolve conflict situations or disagreements in the workplace?
      • Criticise in private, compliment in public. Data driven decisions.
    • Self-Awareness:
      • Do you know your strengths and weaknesses?
      • What can you improve, and why?
      • How do you counterbalance your weaknesses with positive behaviors?
    • Feedback:
      • How have you given feedback to peers?
      • How have you received feedback from peers?
      • What changes resulted from that feedback? Did the individual change? Did you change?

References