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

Senior
Apr 2026

Summary

System design round at OpenAI for a software engineer role. Just one question but it had a lot of surface area and I'm not sure I covered it well.

Questions Asked (1)

Q1

Design a system like chess.com, covering real-time gameplay, matchmaking, and scale.

System DesignTechnical Trade-offsData Modeling
Author's notes

I started with the game state and move validation logic which felt right, but I think I underweighted the real-time communication layer.

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AI HintsAI Generated

Suggested Approach

Start by clarifying functional and non-functional requirements, then sketch a high-level architecture that separates real-time gameplay, matchmaking, and core services. Dive into the trickiest parts—real-time communication and matchmaking—while addressing scalability, consistency, and trade-offs.

Pro tip: Explicitly discuss consistency models (e.g., strong vs. eventual) for game state and matchmaking, and how you'd handle failures gracefully. This shows you think beyond the happy path and understand distributed systems trade-offs.

1. Clarify Requirements

Ask about scale (DAU, concurrent games), latency requirements, game types (real-time vs. turn-based), and features like spectating, chat, and ratings.

2. High-Level Architecture

Outline core components: API gateway, matchmaking service, game service, real-time communication layer (WebSockets), database, and caching. Draw a simple diagram.

3. Deep Dive: Real-Time Gameplay

Explain how moves are transmitted and validated, state synchronization, and conflict resolution. Discuss using WebSockets, game state storage, and consistency models.

4. Deep Dive: Matchmaking

Describe the matchmaking algorithm (e.g., Elo-based), queue management, and how to scale it. Consider latency, fairness, and handling of player disconnects.

5. Scale and Trade-offs

Address scaling strategies: sharding, replication, load balancing, and caching. Discuss trade-offs between consistency, latency, and cost.

Key Points to Mention

  • Use WebSockets for real-time bidirectional communication; consider fallbacks like long polling.
  • Matchmaking algorithm: Elo/Glicko rating system, with dynamic queue ranges based on wait time.
  • Game state consistency: use a central game server or authoritative peer with conflict resolution (e.g., last-write-wins or vector clocks).
  • Database choices: Redis for caching and pub/sub, a relational DB for user data, and possibly a NoSQL store for game logs.
  • Scalability: horizontal scaling of stateless services, sharding by game ID or user ID, and using a message queue for asynchronous tasks.
  • Failure handling: reconnection logic, game state recovery, and graceful degradation during outages.

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