Overview

  • The interview will contain three sections:
    • Algorithms and programming: The candidate will be asked to describe a solution or write code for specific problems. When an English description is rigorous enough, code will not be necessary; but when it is not specified rigorously, code may be needed. We will use codeshare.io to share code. Code is not expected to compile or run, just to demonstrate the solution. Any language among Python, C/C++, C#, or Java is acceptable.
    • Software engineering: We will discuss software engineering issues, system design and organization, and related principles, often using examples.
    • Neural networks: We will discuss the state-of-the-art in the field as well as principles and guidelines for the design and development of complex neural systems.
  • It is also helpful if the candidate can list their level of expertise in the following: Pytorch, Python, Hive, pyhive/pyspark, Anyscale Ray, C/C++, Java, Jax, CUDA, and pytorch lightning. Can you please email me this list so I can share it with John in advance?

Software Engineering

  • Technical Topics Explained

Python, Java, and C/C++

  • Python: An interpreted, high-level, general-purpose programming language known for its simplicity and readability, making it particularly popular for web development, data analysis, artificial intelligence, and scientific computing.
  • Java: A class-based, object-oriented programming language designed to have as few implementation dependencies as possible. It’s widely used for building enterprise-scale applications, Android apps, and web applications.
  • C/C++: C is a procedural programming language supporting structured programming, while C++ is an extension supporting object-oriented programming. Both are known for their speed and efficiency and are commonly used in system/software development, game programming, and applications requiring high-performance computation.

Frameworks and Libraries

  • PyTorch, TensorFlow, and Keras:
    • PyTorch and TensorFlow are open-source machine learning libraries for research and production, offering robust tools for deep learning. PyTorch is known for its dynamic computation graph and user-friendly interface, while TensorFlow offers comprehensive services for a broad set of ML applications.
    • Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It focuses on enabling fast experimentation.
  • Jax: An open-source library for high-performance numerical computing in Python, offering automatic differentiation for high-speed machine learning research.
  • Cuda/Cudnn: CUDA is a parallel computing platform and API model created by Nvidia allowing software developers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing, while CuDNN is NVIDIA’s library of primitives for deep learning networks.
  • Kubernetes: An open-source system for automating deployment, scaling, and management of containerized applications, helping in managing application processes efficiently across a cluster of machines.
  • Spark: Apache Spark is a unified analytics engine for large-scale data processing, offering libraries for SQL, streaming, machine learning, and graph processing.
  • Airflow: Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows, allowing for scheduling and orchestration of complex data pipelines.
  • Kafka: Apache Kafka is a distributed event store and stream-processing platform, designed for high-throughput, fault-tolerant handling of real-time data feeds.
  • Data Warehouse (e.g., Hive): Hive is a data warehousing solution over Hadoop, providing data summarization, query, and analysis. Data warehouses are centralized systems for storing, reporting, and analyzing data from various sources.

Software Engineering Principles

  • Quality Software Engineering and APIs: Emphasizes creating reliable, maintainable, and testable code, developing APIs (Application Programming Interfaces) that allow different software applications to communicate with each other efficiently.

Machine Learning Research and Engineering

  • Involves understanding and implementing the lifecycle of ML models including training, serving predictions, conducting A/B tests, and managing datasets. It also covers the importance of feature engineering, selection, and the validation processes to ensure models are robust and perform well on unseen data.

Domains of Machine Learning

  • Natural Language Processing (NLP): Focuses on the interaction between computers and humans through natural language, aiming to read, decipher, understand, and make sense of human languages in a valuable way.
  • Computer Vision: Deals with how computers can gain high-level understanding from digital images or videos with the goal of automating tasks that the human visual system can do.
  • Personalization and Recommendation: Involves tailoring services or products to individual users’ preferences using algorithms and machine learning techniques to predict what a particular user may prefer among a set of items or content.
  • Sequence Prediction: Involves predicting subsequent elements of a sequence, important in various applications like time-series prediction, speech recognition, and language modeling.
  • Anomaly Detection: The identification of items, events, or observations which do not conform to an expected pattern or other items in a dataset, crucial for fraud detection, network security, and fault detection.

Design Principles

  1. DRY (Don’t Repeat Yourself): This principle advocates for reducing repetition of software patterns. It encourages the abstraction of functionality to prevent code duplication, leading to easier maintenance and updates.

  2. YAGNI (You Aren’t Gonna Need It): Emphasizes avoiding adding functionality until it is necessary. This principle helps in preventing over-engineering and focusing on what’s truly required at the moment.

  3. KISS (Keep It Simple, Stupid): A principle that promotes simplicity in design over complexity. Simple designs are easier to maintain, understand, and extend, reducing the overall cost of development.

  4. Loose Coupling: This principle involves designing systems where each component has, or makes use of, little or no knowledge of the definitions of other separate components. Loose coupling increases the modularity of the system, making it easier to refactor, change, and understand.

  5. High Cohesion: Encourages designing components that are self-contained, with a single, well-defined purpose. High cohesion increases the robustness, reliability, and reusability of components.

  • Modularity: Breaking down the software into smaller, independent, and reusable components or modules. This makes the software easier to understand, test, and maintain.
  • Abstraction: Hiding the implementation details of a module or component and exposing only the necessary information. This makes the software more flexible and easier to change.
  • Encapsulation: Wrapping the data and functions of a module or component into a single unit, and providing controlled access to that unit. This helps to protect the data and functions from unauthorized access and modification.
  • DRY principle (Don’t Repeat Yourself): Avoiding duplication of code and data in the software. This makes the software more maintainable and less error-prone.
  • KISS principle (Keep It Simple, Stupid): Keeping the software design and implementation as simple as possible. This makes the software more understandable, testable, and maintainable.
  • YAGNI (You Ain’t Gonna Need It): Avoiding adding unnecessary features or functionality to the software. This helps to keep the software focused on the essential requirements and makes it more maintainable.
  • SOLID principles: A set of principles that guide the design of software to make it more maintainable, reusable, and extensible. This includes the Single Responsibility Principle, Open/Closed Principle, Liskov Substitution Principle, Interface Segregation Principle, and Dependency Inversion Principle.
  • Test-driven development: Writing automated tests before writing the code, and ensuring that the code passes all tests before it is considered complete. This helps to ensure that the software meets the requirements and specifications.

Architectural Patterns

  1. Event-Driven Architecture (EDA): An architecture that orchestrates behavior around the production, detection, and consumption of events. This pattern is excellent for systems that are highly responsive and adaptable to changes in real-time.

  2. CQRS (Command Query Responsibility Segregation): Separates read and write operations for a data store into distinct interfaces. This pattern can help in scaling applications by allowing reads and writes to be optimized independently.

  3. Domain-Driven Design (DDD): Focuses on the core domain logic of the application and its complexities. DDD advocates modeling software based on the real-world business domain, which can improve communication between technical and non-technical team members and lead to more effective software solutions.

  4. Serverless Architecture: Allows developers to build and run applications and services without managing infrastructure. The cloud provider automatically provisions, scales, and manages the infrastructure required to run the code. This architecture is beneficial for reducing operational costs and complexity.

  5. Microfrontend Architecture: An architectural style where independently deliverable frontend applications compose into a greater whole. It extends the microservices pattern to front-end development, allowing for multiple teams to work independently on different features of the front-end, using different frameworks or technologies.

  • API (Application Programming Interface) design is a crucial aspect of software development, impacting how easily systems can interact with each other. Good API design facilitates easy integration, ensures stability and security, and provides a clear and intuitive way for developers to work with the service. Here are some key principles and best practices for effective API design:

1. Start with the User in Mind

  • Design your API from the consumer’s perspective. It should be intuitive, with clear naming conventions and a logical structure that reflects how developers think about the domain.

2. Use RESTful Principles (When Appropriate)

  • REST (Representational State Transfer) is a popular architectural style for designing networked applications. It uses HTTP requests to access and manipulate web resources using a stateless protocol and standard operations, making it a flexible and widely adopted standard for APIs.
  • Employ HTTP methods explicitly (GET for fetching data, POST for creating data, PUT/PATCH for updates, DELETE for removal).

3. Consistency is Key

  • Ensure consistency in naming conventions, request and response structures, and error handling across the entire API to reduce learning curve and potential confusion.

4. Versioning

  • APIs evolve over time, and versioning helps manage changes without breaking existing integrations. Use a clear and straightforward versioning strategy (e.g., via the URL path, custom request header, or query parameters).

5. Security

  • Implement authentication and authorization protocols like OAuth to protect access to resources. Consider security at every step of the API design to protect sensitive data and ensure privacy.

6. Documentation

  • Comprehensive and clear documentation is crucial for a successful API. It should include detailed descriptions of endpoints, parameters, expected request and response structures, and examples. Tools like Swagger or OpenAPI can automate part of the documentation process and ensure it stays up-to-date.

7. Pagination, Filtering, and Sorting

  • For APIs returning lists of resources, provide options for pagination, filtering, and sorting to allow consumers to easily query the data they need.

8. Rate Limiting

  • Implement rate limiting to prevent abuse and ensure that the API can serve all consumers fairly without being overwhelmed by requests.

9. Use Meaningful HTTP Status Codes

  • Utilize HTTP status codes to communicate the outcome of API requests clearly. For example, use 200 for successful requests, 400 for bad requests, 401 for unauthorized requests, and 500 for internal server errors.

10. Feedback Loop

  • Maintain a feedback loop with your API consumers. Their experiences can provide valuable insights into how your API can be improved.

Neural Networks

  • Upon the user’s query. Searching for San Francisco doesn’t mean you want to stay anywhere in San Francisco, let alone the Bay Area more broadly.
  • Therefore, a great listing in Berkeley shouldn’t come up as the first result for someone looking to stay in San Francisco. Conversely, if a user is specifically looking to stay in the East Bay, their search result page shouldn’t be overwhelmed by San Francisco listings, even if they are some of the highest quality ones in the Bay Area.

  • Build a location relevance signal into our search model that would endeavor to return the best listings possible, confined to the location a searcher wants to stay. One heuristic that seems reasonable on the surface is that listings closer to the center of the search area are more relevant to the query. Given that intuition, we introduced an exponential demotion function based upon the distance between the center of the search and the listing location, which we applied on top of the listing’s quality score.
  • This got us past the issue of random locations, but the signal overemphasized centrality, returning listings predominantly in the city center as opposed to other neighborhoods where people might prefer to stay.

  • To deal with this, we tried shifting from an exponential to a sigmoid demotion curve. This had the benefit of an inflection point, which we could use to tune the demotion function in a more flexible manner. In an A/B test, we found this to generate a positive lift, but it still wasn’t ideal — every city required individual tweaking to accommodate its size and layout. And the city center still benefited from distance-demotion. There are, of course, simple solutions to a problem like this. For example, we could expand the radius for search results and diminish the algorithm’s distance weight relative to weights for other factors. But most locations aren’t symmetrical or axis-aligned, so by widening our radius a search for New York could — gasp — return listings in New Jersey. It quickly became clear that predetermining and hardcoding the perfect logic is too tricky when thinking about every city in the world all at once.

  • So we decided to let our community solve the problem for us. Using a rich dataset comprised of guest and host interactions, we built a model that estimated a conditional probability of booking in a location, given where the person searched. A search for San Francisco would thus skew towards neighborhoods where people who also search for San Francisco typically wind up booking, for example the Mission District or Lower Haight.
  • However, it didn’t take long to realize the biases we had introduced. We were pulling every search to where we had the most bookings, creating a gravitational force toward big cities. A search for a smaller location, such as the nearby surf town Pacifica, would return some listings in Pacifica and then many more in San Francisco. But the urban experience San Francisco offers doesn’t match the surf trip most Pacifica searchers are planning. To fix this, we tried normalizing by the number of listings in the search area. In the case of Pacifica, we now returned other small beach towns over SF. Victory!
  • However by tightening up our search results for Santa Cruz to be great listings in Santa Cruz, the mushroom dome vanished. Thus, we decided to layer in another conditional probability encoding the relationship between the city people booked in and the cities they searched to get there