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Meta·Software Engineer·Technical Phone Screen·Senior

Senior
Jul 2026

Summary

Meta coding screen for a software engineering role, focused entirely on designing and implementing a production-ready FastAPI service from scratch. No algorithms, no LeetCode, just real backend structure and clean code.

Questions Asked (1)

Q1

Design and implement a minimal but production-ready FastAPI service for text summarization, including a POST /summarize endpoint, Pydantic request/response models, in-memory storage of request/response pairs, and a clean separation of router, service, and model layers.

System DesignAPI & IntegrationsTechnical Trade-offs
Author's notes

This was the whole interview, basically.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline the architecture with clear separation of concerns (router, service, model layers). Walk through the implementation details for each layer, emphasizing production-ready aspects like validation, error handling, and testability. Conclude by discussing trade-offs and potential improvements.

Pro tip: Demonstrate production-readiness by mentioning observability (logging, metrics) and graceful degradation, and explicitly state assumptions you make about the summarization model (e.g., mock or external API) to keep the focus on API design.

1. Clarify Requirements and Assumptions

Ask clarifying questions about expected load, summarization model (local vs external), storage persistence, and authentication. State your assumptions to scope the solution.

2. Design the API Contract

Define the POST /summarize endpoint with Pydantic models for request (text, optional parameters) and response (summary, request_id). Include error responses and status codes.

3. Outline Layered Architecture

Describe the router layer (handles HTTP, validation), service layer (business logic, calls summarization model), and model layer (Pydantic schemas, in-memory storage). Explain how dependencies are injected.

4. Implement Core Logic

Walk through code for each layer: router uses service, service uses a summarizer (mock or real) and stores request/response in memory, models define schemas and storage structure.

5. Address Production-Readiness and Trade-offs

Discuss error handling, logging, testing, and scalability. Mention trade-offs of in-memory storage and how to evolve to a database or external service.

Key Points to Mention

  • Separation of concerns: router, service, and model layers for maintainability and testability.
  • Pydantic models for request/response validation and serialization.
  • In-memory storage implementation (e.g., dictionary with request IDs) and its limitations.
  • Error handling and HTTP status codes (400 for bad input, 500 for server errors).
  • Dependency injection for the summarization service to allow mocking in tests.
  • Production considerations: logging, metrics, rate limiting, and async support.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.