Openai·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

Interviewed for an ML Engineer role at OpenAI and got a grid simulation problem that felt more like a competitive programming question than anything ML-related. Not what I expected going in.

Questions Asked (1)

Given an m×n grid where cells are either infected ('X') or healthy ('.'), infection spreads to all 8 neighbors each day. A healthy cell becomes infected if it has at least T infected neighbors. Write a function that returns the number of days until no new infections occur.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Multi-source BFS, which I figured out pretty quickly, but the threshold T tripped me up for a bit.

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AI HintsAI Generated

Suggested Approach

Model the grid as a 2D array and simulate the infection day by day using a multi-source BFS-like approach, but since infection depends on a threshold of infected neighbors, we must recompute the state each day until no changes occur. Use a copy of the grid to avoid overwriting cells within the same day, and count the days until a full pass yields no new infections.

Pro tip: Clarify edge cases upfront: what if T=0 (all healthy cells become infected immediately) or if the grid is already fully infected? Also, discuss potential optimizations like using a queue of cells whose neighbor counts changed, but for an interview, a clear O(m*n*days) simulation is often sufficient.

1. Understand the problem and constraints

Restate the problem: infection spreads to all 8 neighbors each day, but a healthy cell only becomes infected if it has at least T infected neighbors. Ask clarifying questions about T, grid size, and initial state.

2. Choose a simulation strategy

Decide to simulate day by day. Use a copy of the grid to compute the next state, ensuring that infections within the same day do not affect each other. Initialize a day counter.

3. Implement the daily update

For each healthy cell, count infected neighbors (8 directions). If count >= T, mark it infected in the next grid. Track whether any cell changed. If no changes, stop and return the day count.

4. Handle edge cases and optimize if needed

Consider T=0 (all healthy become infected in one day), T>8 (no spread), and already fully infected grid (0 days). Discuss potential optimizations like maintaining a queue of cells with changing neighbor counts.

5. Analyze complexity and trade-offs

Time complexity is O(m*n*days) in the worst case. Space O(m*n). Mention that for large grids, a more efficient approach could use a queue to only process cells whose neighbor counts change, but it adds complexity.

Key Points to Mention

Use a copy of the grid for simultaneous updates to avoid cascading infections within the same day.
Count infected neighbors in all 8 directions (horizontal, vertical, diagonal).
Terminate when a full day passes with no new infections, returning the number of days elapsed.
Handle edge cases: T=0, T>8, already fully infected, no infected cells initially.
Time complexity O(m*n*days) and space O(m*n); discuss potential optimizations.
Consider using a queue to track cells whose neighbor counts change for efficiency.

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