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

SeniorPrefer not to say
May 2026

Summary

System design round at OpenAI for a software engineer role. The whole session was basically one big chess platform question that kept branching into more and more sub-problems. Dense but interesting.

Questions Asked (1)

Q1

Design a real-time online chess platform. Walk through requirements, core entities, real-time move sync, game state persistence, concurrency, spectator support, matchmaking, and how you'd scale the system.

System DesignTechnical Trade-offsData Modeling
Author's notes

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then define core entities and APIs. Focus on real-time move synchronization using WebSockets, game state persistence with a database, and concurrency control. Discuss matchmaking, spectator support, and scaling strategies like sharding and caching.

Pro tip: Emphasize trade-offs: e.g., using WebSockets for real-time but falling back to polling for reliability, and using optimistic locking for concurrency. Show awareness of failure modes and how to handle them.

1. Clarify Requirements and Scale

Ask about expected user base, concurrent games, latency requirements, and features like rating, chat, and tournaments. Define functional and non-functional requirements.

2. Define Core Entities and Data Model

Identify entities: User, Game, Move, Board, Player, Spectator. Design schemas for persistence, considering move history and game state snapshots.

3. Design Real-Time Move Sync and Game Logic

Use WebSockets for bidirectional communication. Server validates moves, updates game state, and broadcasts to players and spectators. Ensure move ordering and idempotency.

4. Handle Concurrency and Persistence

Use optimistic locking or versioning to prevent race conditions. Persist moves and game state to a database (e.g., PostgreSQL) with write-ahead logging or event sourcing for durability.

5. Matchmaking, Spectator Support, and Scaling

Implement matchmaking via a queue with rating-based pairing. For spectators, use pub/sub to broadcast moves. Scale horizontally with load balancers, sharding by game ID, and caching (Redis) for active games.

Key Points to Mention

  • WebSocket vs. HTTP polling for real-time communication, with fallback strategies.
  • Concurrency control: optimistic locking, version numbers, or distributed locks.
  • Game state persistence: event sourcing vs. snapshotting, and database choice.
  • Matchmaking algorithms: Elo/MMR-based, with queue management.
  • Spectator support: pub/sub pattern, separate channels, and scalability.
  • Scaling: sharding, caching, load balancing, and handling reconnections.

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