Openai·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

Apr 2026

Summary

OpenAI ML Engineer interview that was more software design than ML. The whole thing centered on building a turn-based game simulation, and the real test was how cleanly you could model it and how gracefully your design held up as they kept piling on new requirements.

Questions Asked (2)

Design and implement a two-player turn-based game simulation (like tic-tac-toe). Model the players, board state, turn logic, and win/draw conditions, and expose a clean API for starting a game, taking a turn, querying state, and detecting when the game is over.

System DesignAPI & IntegrationsData Modeling

Author's notes

I went straight to the board representation and kind of forgot to think about the API surface first, which bit me later.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and defining the core entities (Player, Board, Game) and their responsibilities. Then design a clean API with methods for starting, taking turns, querying state, and checking game over, and outline the turn logic and win/draw detection. Finally, discuss implementation details, edge cases, and potential extensions like AI players or different board sizes.

Pro tip: Emphasize separation of concerns: keep game rules (win/draw detection) separate from game state and player interaction. This makes the design extensible and testable, which is crucial for ML engineering where modularity and reproducibility matter.

1. Clarify Requirements and Scope

Ask clarifying questions about board size, number of players, win conditions, and whether the game should support AI players or network play. Define the minimal viable product and potential extensions.

2. Define Core Entities and Responsibilities

Identify key classes/objects: Player (with symbol), Board (state and operations), Game (orchestrates turns and rules). Assign clear responsibilities to each to ensure separation of concerns.

3. Design the API

Specify methods for starting a game, taking a turn (with validation), querying the board state, and checking if the game is over (win/draw). Consider return types and error handling.

4. Implement Turn Logic and Win/Draw Detection

Describe the turn-taking mechanism, including validation of moves and switching players. Outline algorithms for checking win conditions (e.g., rows, columns, diagonals) and draw detection.

5. Discuss Extensibility and Testing

Mention how the design can be extended (e.g., different board sizes, AI players) and how to test the game logic (unit tests for win conditions, turn validation).

Key Points to Mention

Separation of concerns: Board handles state, Game handles rules and turn logic, Player represents participant.
API design: clear method signatures (e.g., start_game(), make_move(player, position), get_state(), is_game_over()).
Turn validation: ensure moves are legal (correct player, empty cell, game not over).
Win/draw detection: efficient checks for rows, columns, diagonals; draw when board full and no winner.
Extensibility: use of interfaces or abstract classes for players to allow AI or human players.
Testing: unit tests for win conditions, turn alternation, and invalid moves.

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

How does your design handle iterative feature additions like variable board sizes, more than two players, alternative win conditions, undo/replay, and persistence? What would you refactor and what are the trade-offs?

Technical Trade-offsSystem DesignAdaptability & Ambiguity

Author's notes

This is where it got uncomfortable.

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AI HintsAI Generated

Suggested Approach

Start by outlining a clean, modular design that separates core game logic from presentation and persistence layers. Then, for each extension, explain how you would adapt the design (e.g., using strategy patterns, dependency injection, or event sourcing) and discuss the trade-offs in terms of complexity, performance, and maintainability. Emphasize that you prioritize extensibility but avoid over-engineering until requirements are clear.

Pro tip: Demonstrate awareness of the cost of premature abstraction: propose a simple, working solution first, then refactor when the need arises, using tests to ensure safe evolution.

1. Clarify requirements and constraints

Ask clarifying questions about expected scale, performance needs, and whether these features are likely or just hypothetical. This shows you don't over-engineer without context.

2. Describe the baseline design

Briefly outline a modular architecture (e.g., MVC, entity-component-system) that separates game state, rules, and I/O. Highlight interfaces that can be extended.

3. Address each extension

For each feature (variable board sizes, >2 players, alternative win conditions, undo/replay, persistence), explain how you would implement it with minimal changes, referencing design patterns like Strategy, Command, or Memento.

4. Discuss trade-offs and refactoring

For each extension, analyze the trade-offs: added complexity, performance overhead, impact on existing code, and testing burden. Mention what you would refactor and why.

5. Summarize and prioritize

Conclude with a pragmatic approach: which features to implement first based on likelihood and value, and how to keep the design flexible without over-engineering.

Key Points to Mention

Separation of concerns: isolate game rules from UI, persistence, and player input.
Use of design patterns: Strategy for win conditions, Command for undo/replay, Observer for state changes.
Data-driven design: represent board size, player count, and win conditions as configuration or parameters.
Event sourcing or command pattern for undo/replay and persistence, with trade-offs in memory and complexity.
Testing strategy: unit tests for core logic, integration tests for extensions, and property-based testing for variable configurations.
Trade-offs: flexibility vs. complexity, performance vs. generality, and development time vs. future-proofing.

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