Openai·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Got a coding round for an ML Engineer role at OpenAI and the problem was a grid simulation with a pretty nasty extension tacked on. The core mechanics weren't too bad but the optimization part at the end had me sweating.

Questions Asked (2)

You have an R by C grid of plants, each in one of four states: healthy, infected, recovered, or dead. Given infection thresholds T and K, a delay D, and a simulation length N, implement a day-by-day simulator where plants transition states based on how many infected neighbors they have, with all updates applied simultaneously from a snapshot taken at the start of each day.

Algorithms & Data StructuresSystem Design

Author's notes

The simultaneous update rule is where I initially tripped up.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the state transition rules and parameters, then outline a simulation loop that snapshots the grid each day, computes neighbor counts, and applies all updates simultaneously. Emphasize modular design with separate functions for neighbor counting and state transitions, and discuss how to handle edge cases and performance for large grids.

Pro tip: Mention that you would use a double-buffer approach (two grids) to ensure simultaneous updates, and that you'd consider vectorizing neighbor counts with convolution or array operations for efficiency.

1. Clarify Rules and Parameters

Ask questions to confirm the exact transition rules for each state based on infected neighbor count, thresholds T and K, and delay D. Ensure you understand how recovered and dead states behave and whether they can change.

2. Design Data Structures

Choose a representation for the grid (e.g., 2D array of enums or integers) and plan to use two buffers to hold the current and next states for simultaneous updates.

3. Implement Neighbor Counting

Write a function to count infected neighbors for each cell, handling boundaries. Consider using convolution or vectorized operations for performance.

4. Simulate Day-by-Day

Loop for N days: snapshot the grid, compute next state for each cell based on neighbor counts and rules, then swap buffers. Ensure all updates are based on the snapshot.

5. Test and Optimize

Test with small grids and edge cases (e.g., all infected, no infected). Discuss time complexity and potential optimizations like parallelization or sparse representations.

Key Points to Mention

Simultaneous updates using double buffering to avoid state propagation within a single day.
Neighbor counting with boundary handling (e.g., using padding or conditional checks).
State transition rules: how thresholds T and K determine transitions from healthy to infected, and the role of delay D.
Time complexity: O(N * R * C) for naive approach, and potential optimizations like convolution or vectorization.
Edge cases: grids with no infected plants, all infected, or boundary cells.
Modular design: separate functions for neighbor counting, state transition, and simulation loop for clarity and testability.

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

Before the simulation begins, you can burn exactly one full row or one full column, instantly killing all plants in it. Which row or column minimizes the total number of dead plants after N days? Return the minimum dead count and the action taken, breaking ties arbitrarily.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This is where things got rough.

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AI HintsAI Generated

Suggested Approach

Model the problem as a grid where each plant has a growth pattern over N days, and precompute the total dead plants for each possible row or column burn by simulating the effect of the burn on day 0. Then, for each candidate row/column, compute the final dead count after N days, and select the one that minimizes this count. Since the grid size is likely small, a brute-force simulation for each row and column is feasible.

Pro tip: Clarify the growth and death rules upfront—ambiguity here can derail your solution. Also, consider edge cases like N=0 or when burning a row/column kills all plants immediately, and mention that ties can be broken arbitrarily as per the problem.

1. Understand the simulation rules

Clarify how plants grow, spread, or die over N days, and how burning a row/column affects the initial state. Ensure you know whether the burn happens before day 1 and how it influences subsequent days.

2. Precompute baseline outcomes

Simulate the N-day process without any burn to understand the natural progression, or precompute the contribution of each cell to the total dead count if not burned.

3. Evaluate each row and column burn

For each row and each column, simulate the N-day process with that row/column burned on day 0, and record the total dead plants after N days.

4. Select the optimal action

Compare the dead counts from all row and column burns, and choose the one with the minimum dead count. If there's a tie, pick arbitrarily.

5. Return the result

Output the minimum dead count and the action (e.g., 'burn row 3' or 'burn column 5').

Key Points to Mention

Time complexity: O((R+C) * N * R * C) if simulating naively, but can be optimized by precomputing effects.
Space complexity: O(R*C) to store the grid state.
Edge cases: N=0, all plants already dead, burning a row/column that contains no plants.
Tie-breaking: any row/column with the same minimum dead count is acceptable.
Optimization: if the grid is large, consider using prefix sums or dynamic programming to avoid re-simulating from scratch for each burn.
Clarify whether the burn kills plants instantly and whether they can regrow or spread from other cells.

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