Openai·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Did a coding round for an ML Engineer role at OpenAI and got a multi-part grid infection problem. Five parts total, each one layering on more rules. It was a lot to get through in one session.

Questions Asked (1)

Simulate how an infection spreads across a 2D grid of plants, starting from initially infected cells and spreading to healthy neighbors each time step. Solve this across five progressively harder variants that add rules like immunity, variable spread rates, multiple infection sources, and grid mutations. For each part, return either the total time to full infection or the grid state after k steps.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Basically a rotting oranges problem but they kept piling on rules.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and assumptions for each variant, then model the infection spread as a multi-source BFS on a grid where each step represents one time unit. For each variant, adapt the BFS by incorporating additional state (e.g., immunity, variable spread rates) and carefully handle edge cases like grid mutations. Discuss trade-offs between BFS and simulation, and consider optimizations for large grids.

Pro tip: Demonstrate awareness of real-world ML applications by relating the simulation to epidemic modeling or cellular automata, and mention how you would validate the solution with unit tests for each variant.

1. Clarify requirements and constraints

Ask clarifying questions about grid size, infection rules, time steps, and output format for each variant. Confirm assumptions like 4-directional spread and synchronous updates.

2. Design core BFS simulation

Implement a multi-source BFS where initially infected cells are enqueued at time 0, and each step processes all current infections to spread to healthy neighbors. Track time and grid state.

3. Extend for each variant

Modify the BFS to handle immunity (skip immune cells), variable spread rates (use priority queue or time-based scheduling), multiple sources (already handled by multi-source BFS), and grid mutations (update grid dynamically and adjust BFS).

4. Analyze complexity and trade-offs

Discuss time and space complexity (O(N*M) for BFS) and compare with alternative approaches like cellular automata simulation. Mention optimizations for large grids or many steps.

5. Test and validate

Outline test cases for each variant, including edge cases like no spread, full immunity, and mutations causing disconnections. Verify outputs for small grids manually.

Key Points to Mention

Multi-source BFS as the core algorithm for simultaneous spread
Handling of immunity and variable spread rates via state tracking or priority queues
Time complexity O(N*M) and space complexity O(N*M) for grid storage and queue
Trade-offs between BFS and discrete simulation for dynamic mutations
Edge cases: initially infected cells, no healthy neighbors, mutations creating isolated regions
Potential optimizations: early termination when no new infections, using bitsets for large grids

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