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Openai·Software Engineer·Onsite - Coding / Algorithms·Intermediate

IntermediateRejected
Jun 2026

Summary

Coding round at OpenAI for a Software Engineer role. Five problems, I only got through three of them, and I never even saw what the last two were asking. Probably didn't pass.

Questions Asked (1)

Q1

Solve the 'plant infection' problem and its follow-up variations across a set of five progressively harder subproblems.

Algorithms & Data Structures
Author's notes

Got through the first three parts okay but was writing in C++ and just too slow.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem statement and constraints for each subproblem, then solve them in order of increasing difficulty, explaining your reasoning and trade-offs. For each, discuss brute force first, then optimize using appropriate algorithms and data structures, and analyze time/space complexity.

Pro tip: Demonstrate strong communication by thinking aloud and proactively discussing edge cases and potential optimizations, even if you don't finish all subproblems. Show that you can adapt your approach as the problem evolves.

1. Clarify the problem

Ask questions to understand the input/output format, constraints, and what each subproblem entails. Confirm assumptions before proceeding.

2. Start with a brute-force solution

For the first subproblem, describe a simple, correct approach even if inefficient. This establishes a baseline and shows you can solve the problem.

3. Optimize incrementally

For each subsequent subproblem, identify bottlenecks in the previous solution and propose improvements using better algorithms or data structures.

4. Analyze complexity and trade-offs

For each solution, state the time and space complexity, and discuss any trade-offs between different approaches.

5. Test with examples and edge cases

Walk through small examples and edge cases (e.g., empty input, large values) to verify correctness and robustness.

Key Points to Mention

  • Graph traversal algorithms (BFS/DFS) for modeling infection spread
  • Union-Find (Disjoint Set Union) for connectivity problems
  • Dynamic programming or greedy approaches for optimization variations
  • Time and space complexity analysis for each solution
  • Handling edge cases such as disconnected components or cycles
  • Trade-offs between different data structures (e.g., adjacency list vs. matrix)

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