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

SeniorPrefer not to say
Jun 2026

Summary

System design round at Meta for a software engineering role. The prompt was to design a LeetCode-style coding platform end-to-end, which sounds straightforward until you start thinking about sandboxed code execution at scale. A lot of ground to cover in one session.

Questions Asked (1)

Q1

Design an online coding practice and interview platform similar to popular coding challenge sites. Cover the full system: architecture, data modeling, code execution with sandboxing, scaling the judge layer, caching, and how you'd evolve the design over time.

System DesignTechnical Trade-offsData Modeling
Author's notes

This one ate up a lot of time because I kept wanting to jump straight into the execution sandbox piece, which is the fun part, but the interviewer kept pulling me back to basics like auth flows and problem storage.

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AI HintsAI Generated

Suggested Approach

Start by clarifying functional and non-functional requirements, then sketch a high-level architecture covering the main components: web app, API, judge, and data stores. Dive deep into the judge's sandboxing and scaling, and discuss trade-offs and evolution. Use back-of-the-envelope calculations to justify design choices.

Pro tip: Emphasize security and isolation in the code execution sandbox, as it's the most critical and challenging part. Also, proactively discuss how you'd handle malicious code and resource limits, showing you think like a production engineer.

1. Clarify Requirements

Ask questions to understand scale, supported languages, execution time limits, and expected user base. Define functional (submit code, run tests, view results) and non-functional (latency, availability, security) requirements.

2. High-Level Architecture

Outline the main components: client apps, API gateway, user service, problem service, submission service, judge service, and data stores. Explain how they interact and the flow of a code submission.

3. Data Modeling

Design schemas for users, problems, submissions, and test cases. Discuss SQL vs NoSQL choices, indexing, and how to store code and results efficiently.

4. Code Execution & Sandboxing

Detail the judge's execution environment: containerization (Docker), resource limits (CPU, memory, time), isolation (namespaces, seccomp), and security measures to prevent escapes and abuse.

5. Scaling & Evolution

Explain how to scale the judge horizontally with a queue and worker pool, use caching for problems and results, and evolve the design for higher concurrency, new languages, and real-time feedback.

Key Points to Mention

  • Use of message queues (e.g., Kafka, RabbitMQ) to decouple submission and judging for scalability and fault tolerance.
  • Sandboxing techniques: containers, gVisor, Firecracker, or seccomp for secure code execution.
  • Caching strategies: Redis for session data, problem metadata, and leaderboard; CDN for static assets.
  • Database choices: SQL for transactional data (users, submissions), NoSQL for logs and test cases.
  • Auto-scaling judge workers based on queue depth and monitoring for resource utilization.
  • Trade-offs: consistency vs availability, latency vs security, and cost vs performance.

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