Openai·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jul 2026

Summary

Applied for a Software Engineer role at OpenAI and hit a system design screen on Chess. Felt decent about it, then found out there'd be a second SD round anyway.

Questions Asked (1)

Design a system to support a Chess application.

System DesignData ModelingTechnical Trade-offs

Author's notes

Walked through it and thought I covered the bases pretty well.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: is this a single-player game, multiplayer, or both? Then focus on the core domain model (board, pieces, moves) and the real-time multiplayer architecture, including state synchronization and move validation. Finally, discuss trade-offs in data storage, scalability, and matchmaking.

Pro tip: Emphasize the importance of move validation and game state consistency, especially in multiplayer scenarios, and propose using a server-authoritative model with event sourcing for auditability and replay.

1. Clarify Requirements

Ask about expected scale, real-time vs. turn-based, single-player vs. multiplayer, and features like matchmaking, chat, and replay.

2. Design Core Domain Model

Define the board representation, piece types, move generation, and validation rules. Consider using bitboards for efficiency.

3. Architecture for Multiplayer

Choose a server-authoritative model with WebSockets for real-time communication. Design for state synchronization, move validation on server, and handling disconnections.

4. Data Storage and Scalability

Decide on databases for user profiles, game history, and ongoing games. Use caching and sharding for scalability. Consider event sourcing for game state.

5. Trade-offs and Extensions

Discuss trade-offs between consistency and latency, SQL vs. NoSQL, and how to extend to features like AI opponents or tournaments.

Key Points to Mention

Use a server-authoritative model to prevent cheating and ensure consistency.
Represent the board efficiently (e.g., bitboards) for move generation and validation.
Employ WebSockets for real-time communication between clients and server.
Store game state and moves using event sourcing for replay and auditability.
Design matchmaking using a queue system and rating algorithms like Elo or Glicko.
Consider scalability with horizontal scaling, caching, and database sharding.

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