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

IntermediatePrefer not to say
May 2026

Summary

OpenAI software engineer coding round with a multi-part problem. Got through most of it but ran out of time before fully finishing the last part I attempted, which stung a bit.

Questions Asked (1)

Q1

Solve the 'plant infection' multi-part coding problem, which progressively builds in complexity across several parts.

Algorithms & Data Structures
Author's notes

Got through parts 1-3 fine and was close on part 4, but time ran out before I could debug the failing test cases.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem statement and constraints for each part, then solve incrementally from brute force to optimal, explaining your reasoning and trade-offs. Test with edge cases and ensure your solution is clean and efficient before moving to the next part.

Pro tip: Demonstrate strong communication by thinking aloud and proactively discussing time/space complexity for each part. If stuck, simplify the problem and build up, showing resilience and problem-solving skills.

1. Understand the Problem

Ask clarifying questions to fully grasp the problem for each part, including input/output formats, constraints, and edge cases.

2. Start with a Brute Force Solution

Outline a simple, correct approach first, even if inefficient, to establish a baseline and ensure understanding.

3. Optimize Incrementally

Analyze bottlenecks and improve the solution step by step, explaining the trade-offs and complexity at each stage.

4. Test and Validate

Walk through examples, including edge cases, to verify correctness and discuss potential pitfalls.

5. Reflect and Extend

Summarize the solution, discuss possible extensions or variations, and relate to real-world applications if relevant.

Key Points to Mention

  • Time and space complexity analysis for each part
  • Edge cases and how to handle them
  • Trade-offs between different approaches (e.g., BFS vs DFS, recursion vs iteration)
  • Modular and readable code structure
  • Testing strategy and validation
  • Potential optimizations and scalability

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