Openai·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026

Summary

OpenAI SWE interview with a multi-part simulation problem centered on modeling plant infection spread across a farm field. The question built up incrementally across five parts, each layering on new rules or metrics. Pretty intense for a single coding round.

Questions Asked (1)

Implement a multi-part plant infection spread simulator over a farm grid, where each part adds new rules or metrics such as state updates, neighborhood spread logic, infection counts, peak statistics, time-step queries, and mechanics like immunity, recovery, or barriers.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

Five parts back to back, each one depending on your previous code being clean enough to extend.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem scope and constraints, then design a modular simulation architecture that separates state representation, update rules, and metrics collection. Implement incrementally, validating each part with small test cases before adding complexity like immunity or barriers.

Pro tip: Emphasize extensibility: design the simulator so new rules (e.g., immunity, barriers) can be plugged in without rewriting core logic. This shows foresight and aligns with OpenAI's emphasis on scalable systems.

1. Clarify Requirements and Constraints

Ask questions to understand grid size, infection rules, time-step semantics, and expected outputs. Confirm whether the simulation is deterministic and how metrics should be reported.

2. Design Data Structures and State Representation

Choose an efficient grid representation (e.g., 2D array) and define cell states (healthy, infected, immune, etc.). Plan how to track infection counts and peak statistics over time.

3. Implement Core Simulation Loop

Write the time-step update logic: for each cell, apply spread rules based on neighbors, handle state transitions (infection, recovery, immunity), and update metrics. Ensure barriers are respected.

4. Add Metrics and Query Support

Implement functions to query infection counts at a given time, peak infection stats, and other required metrics. Consider precomputing or caching results for efficiency.

5. Test and Validate with Edge Cases

Create small test grids to verify spread logic, immunity, recovery, and barriers. Check performance for larger grids and discuss trade-offs (e.g., time vs. space).

Key Points to Mention

Modular design: separate simulation rules from state and metrics for extensibility.
Efficient neighbor iteration: use direction arrays and boundary checks to avoid errors.
State transition handling: clearly define order of operations (e.g., simultaneous updates).
Metrics tracking: maintain running counts and peaks to answer queries in O(1) or O(log n).
Trade-offs: discuss time/space complexity and potential optimizations (e.g., sparse representation).
Testing strategy: unit tests for each rule and integration tests for multi-part scenarios.

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