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

SeniorPrefer not to say
Apr 2026

Summary

Meta system design round for a software engineer role. One massive question covering basically every layer of a coding judge platform. Walked out unsure if I nailed it or rambled for 45 minutes.

Questions Asked (1)

Q1

Design an online coding practice and judging platform similar to LeetCode or Codeforces. Cover the full end-to-end architecture including user accounts, problem browsing, multi-language submission intake, sandboxed code execution, async grading with a distributed worker fleet, storage and data models, test-data versioning and re-judging, plagiarism detection, rate limiting, caching, observability, and how the system scales to contest spikes while handling worker crashes and queue redelivery correctly.

System DesignTechnical Trade-offsData Modeling
Author's notes

This was basically ten questions duct-taped into one.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., number of users, submissions per day, languages supported, contest spikes), then sketch a high-level architecture with clear separation of concerns: API gateway, submission service, message queue, distributed workers, and storage. Dive into the critical path of code execution and grading, emphasizing sandboxing, async processing, fault tolerance, and data consistency, while touching on all requested aspects.

Pro tip: Emphasize idempotency and exactly-once processing in the grading pipeline: use unique submission IDs, deduplicate results, and design workers to be stateless so they can be safely retried after crashes. This shows you understand real-world distributed systems pitfalls.

1. Clarify Requirements and Scale

Ask about expected user base, submission volume, supported languages, contest frequency, and latency requirements. Establish functional and non-functional goals to guide design decisions.

2. High-Level Architecture

Outline core components: API gateway, user service, problem service, submission service, message queue (e.g., Kafka), worker fleet, result store, and databases. Explain data flow from submission to result.

3. Deep Dive into Critical Components

Detail sandboxed execution (e.g., containers, seccomp, cgroups), async grading with distributed workers, queue redelivery and idempotency, test-data versioning, and re-judging. Discuss trade-offs (e.g., latency vs. isolation).

4. Address Cross-Cutting Concerns

Cover rate limiting, caching (e.g., problem statements, leaderboards), plagiarism detection (e.g., token-based similarity), observability (metrics, logging, tracing), and security (sandbox escapes, DDoS).

5. Scaling and Fault Tolerance

Explain how to scale to contest spikes (auto-scaling workers, queue buffering), handle worker crashes (retries, dead-letter queues), and ensure data consistency (transactions, eventual consistency).

Key Points to Mention

  • Sandboxing techniques: containers, seccomp, cgroups, and resource limits (CPU, memory, time) to isolate untrusted code.
  • Async grading pipeline: message queue (Kafka/RabbitMQ) with at-least-once delivery, idempotent workers, and deduplication to handle redelivery.
  • Test-data versioning: store test cases with version tags; re-judging triggered by version changes, with efficient reprocessing of past submissions.
  • Plagiarism detection: use token-based or AST-based similarity checks, possibly with locality-sensitive hashing, and run asynchronously.
  • Rate limiting and caching: token bucket per user/IP, cache problem statements and leaderboards with Redis, and use CDN for static assets.
  • Observability: metrics (submission latency, queue depth, worker health), distributed tracing, and logging for debugging and capacity planning.

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