Openai·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Got a pretty gnarly algorithmic problem at what seemed like a technical phone screen for a software engineer role at OpenAI. One question, lots of moving parts, and I left unsure if I'd even fully understood the problem statement by the time the call ended.

Questions Asked (1)

You have an N×M grid where some cells start infected. Infection spreads to 4-directional neighbors one step per day, and an infected plant dies if it ever had K or more infected neighbors simultaneously within D days of getting infected. You can burn (permanently remove) exactly one row or one column at the start of any single day. Find the optimal day and row/column to burn in order to minimize total plant deaths.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I spent the first few minutes just trying to re-read the problem back to the interviewer because I kept confusing the death condition with the spread condition.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem constraints and assumptions, especially the exact death condition and whether burning removes the row/column permanently. Then, propose a simulation-based approach that models infection spread and death counts for each possible burn day and line, and discuss how to optimize the search using pruning or incremental updates.

Pro tip: Demonstrate awareness of the trade-off between simulation accuracy and computational feasibility; mention that for large grids, a full simulation per candidate may be too slow, so you'd consider precomputing infection times and neighbor counts to evaluate burns efficiently.

1. Clarify problem details

Ask about edge cases: does burning a row/column remove already infected plants? Does the death condition apply only to plants that get infected, or also to initially infected ones? What are typical N, M, K, D values?

2. Model infection and death

Simulate the infection spread day by day, tracking for each cell its infection day and the number of infected neighbors it has over time. Determine when a cell meets the death condition (K or more infected neighbors simultaneously within D days of infection).

3. Enumerate burn options

For each possible burn day (from day 0 up to when infection ends) and each row/column, simulate the effect of burning that line at that day. Count total deaths.

4. Optimize the search

If brute force is too slow, propose optimizations: precompute infection times without burns, then for each burn, only update affected cells; use pruning based on early death counts; or consider that burning early may prevent more infections but might not reduce deaths if deaths are driven by initial infections.

5. Analyze complexity and trade-offs

Discuss time and space complexity of the approach, and trade-offs between exact simulation and heuristics. Mention that the optimal burn might be on a day before any deaths occur, and that burning a line with many infected cells could reduce neighbor counts for adjacent cells.

Key Points to Mention

Clarify the death condition: 'simultaneously within D days' means we need to check if at any time point within D days after infection, the cell has >= K infected neighbors.
Burning a row/column removes it permanently, so it also removes any infected plants there, potentially reducing infections in neighbors.
The optimal burn day could be before the infection reaches the line, to prevent spread, or after, to reduce neighbor counts for dying cells.
Simulation can be done with BFS for infection spread, and for each cell, track the maximum number of infected neighbors over the D-day window.
For efficiency, precompute infection times and neighbor counts without burns, then for each burn, only recompute affected regions.
Consider that burning a line might not always reduce deaths; sometimes it could increase deaths if it removes a buffer that would have absorbed infections.

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