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Openai·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026

Summary

OpenAI SWE coding round where I had to implement something related to typing in a language, basically an IME-style problem. Details were sparse even in the original post so I'm going off memory here.

Questions Asked (1)

Q1

Implement a function that simulates typing in a language using an input method editor (IME) style system, and make it pass a set of provided test cases.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The problem statement itself wasn't totally clear to me at first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the IME behavior and test cases, then design a state machine that tracks composition state and buffer. Implement incrementally, testing against provided cases, and discuss trade-offs like time/space complexity and edge cases.

Pro tip: Demonstrate maturity by proactively discussing how to handle edge cases like invalid input, buffer overflow, and Unicode, and by suggesting a test-driven approach.

1. Understand Requirements

Ask clarifying questions about the IME behavior, expected input/output, and test cases. Identify key operations like start composition, update, commit, and cancel.

2. Design State Machine

Model the IME as a state machine with states (idle, composing) and transitions based on input events. Define the buffer and how it updates.

3. Implement Core Logic

Write the function to handle each event, updating state and buffer accordingly. Ensure it passes the provided test cases incrementally.

4. Test and Refine

Run the test cases, debug failures, and handle edge cases like empty input, invalid sequences, and buffer limits.

5. Discuss Trade-offs

Analyze time/space complexity, alternative designs (e.g., using a stack vs. string buffer), and potential improvements.

Key Points to Mention

  • State machine design for IME composition
  • Buffer management and string manipulation
  • Edge cases: invalid input, buffer overflow, Unicode handling
  • Time and space complexity analysis
  • Test-driven development and incremental testing
  • Trade-offs between different data structures for the buffer

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