Openai·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jul 2026

Summary

Interviewed for a software engineering role at OpenAI and got hit with a grid simulation problem that looked like a BFS variant but had enough wrinkles to slow me down. The death-marking mechanic was the part that tripped me up at first.

Questions Asked (1)

You're given an m x n grid where each cell is in one of four states: uninfected, infected, immune, or dead. Two thresholds control the spread. An uninfected cell gets infected if at least infectThreshold of its 8 neighbors are currently infected. When a cell gets infected, if at least deathThreshold of those same neighbors are infected, the cell is marked to die instead of recovering. Infected cells last exactly one day then resolve. Simulate this until the grid stops changing and return the final state.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The simultaneous update rule is what gets you if you're not careful.

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AI HintsAI Generated

Suggested Approach

Clarify the rules and edge cases first, then outline a simulation using two grids to represent the current and next states. Emphasize the importance of simultaneous updates and termination conditions, and discuss time/space complexity trade-offs.

Pro tip: Mention that you would use a double-buffer approach to avoid state mutation issues, and that you'd validate the simulation with small test cases before scaling up.

1. Clarify rules and edge cases

Ask about neighbor counting (8-directional), threshold semantics (>=), and what happens if a cell meets both infection and death conditions. Confirm initial states and termination criteria.

2. Choose data structures

Use two 2D arrays (or lists of lists) to represent the current and next grid states. This avoids overwriting cells before their neighbors are processed.

3. Simulate day by day

For each day, iterate over all cells, compute infected neighbor counts, and update the next grid based on the rules. Infected cells become dead or uninfected after one day.

4. Detect termination

After each day, compare the new grid with the previous one. If no changes occur, stop and return the final grid.

5. Analyze complexity and optimize

Discuss time complexity O(days * m * n) and space O(m * n). Consider optimizations like tracking active cells or using a queue if the grid is sparse.

Key Points to Mention

Simultaneous update requirement: use double buffering to avoid using partially updated state.
Neighbor counting: 8-directional, handle boundaries carefully.
Threshold conditions: infection if >= infectThreshold, death if >= deathThreshold among infected neighbors.
State transitions: infected cells last exactly one day, then become dead or uninfected.
Termination: stop when grid stops changing (no new infections or deaths).
Complexity: O(days * m * n) time, O(m * n) space; potential optimizations for sparse grids.

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