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Meta·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
May 2026

Summary

Meta system design round, one big question about building a code judging platform from scratch. Pretty dense scope for a single session but they seemed more interested in how you structured the tradeoffs than whether you hit every component.

Questions Asked (1)

Q1

Design a system like LeetCode: users can browse problems, submit code, get it judged in a sandbox against test cases, and see a verdict along with runtime and memory stats. Consider problem storage, the submission pipeline, sandboxed multi-language execution with resource limits, queuing, results storage, and optional leaderboard or contest features.

System DesignTechnical Trade-offsData Modeling
Author's notes

This one sprawled fast.

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AI HintsAI Generated

Suggested Approach

Start by clarifying functional and non-functional requirements, then sketch a high-level architecture covering problem storage, submission pipeline, sandboxed execution, and results storage. Dive deep into the most critical components like the sandbox and queuing system, discussing trade-offs and scalability.

Pro tip: Emphasize security and isolation in the sandbox, as it's the most challenging part; mention using containers with seccomp and cgroups, and consider pre-warming sandboxes to reduce latency.

1. Requirements Clarification

Ask about scale (users, submissions per day), supported languages, latency expectations, and features like contests/leaderboards. Define functional and non-functional requirements.

2. High-Level Architecture

Outline main components: API gateway, problem service, submission service, judge workers, result store, and optional leaderboard service. Sketch data flow from submission to verdict.

3. Deep Dive: Sandbox & Execution

Explain how to run untrusted code securely: use containers (Docker) with resource limits (CPU, memory, time), network isolation, and seccomp. Discuss multi-language support via pre-built images.

4. Scaling & Reliability

Discuss queuing (e.g., Kafka, RabbitMQ) to handle bursts, auto-scaling judge workers, and ensuring fault tolerance. Address result storage (e.g., NoSQL for submissions, SQL for problems).

5. Trade-offs & Extensions

Talk about trade-offs: synchronous vs asynchronous judging, caching, and consistency. Mention optional features like leaderboards (using Redis sorted sets) and contest handling.

Key Points to Mention

  • Sandbox security: isolation using containers, seccomp, cgroups, and resource limits.
  • Queuing system: decoupling submission from execution, handling spikes with message queues.
  • Multi-language support: pre-built Docker images per language, versioning.
  • Data modeling: problems stored in relational DB, submissions in NoSQL for scalability.
  • Result storage and retrieval: storing verdict, runtime, memory; efficient querying for user history.
  • Leaderboard/contest: real-time ranking with Redis sorted sets, contest-specific submission handling.

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