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Openai·Software Engineer·Online Assessment (OA)·Intermediate

IntermediatePrefer not to say
May 2026Remote

Summary

Got an OA for a SWE role at OpenAI and it was basically a straightforward key-value store implementation. Nothing too crazy, but the constraints meant you had to actually think about efficiency rather than just brute-forcing it.

Questions Asked (1)

Q1

Build an in-memory key-value store from scratch that supports PUT, GET, DEL, and COUNT operations, reading from stdin and printing results to stdout within roughly linear time overall.

Algorithms & Data StructuresSystem Design
Author's notes

Looks dead simple until you actually sit down and think about COUNT.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then propose a hash map-based design for O(1) average-case operations. Discuss how to handle input parsing efficiently and ensure overall linear time by processing each command in constant time.

Pro tip: Mention that you would use a buffered reader and writer to handle large input/output efficiently, and consider edge cases like duplicate keys and deletion of non-existent keys.

1. Clarify Requirements

Ask about input format, expected scale, and any constraints on memory or time. Confirm that operations are case-sensitive and that COUNT returns the number of keys.

2. Choose Data Structure

Select a hash map (e.g., unordered_map in C++ or dict in Python) for O(1) average-case PUT, GET, DEL, and COUNT. Discuss potential collisions and resizing.

3. Design I/O Handling

Read commands line by line from stdin, parse them, execute the operation, and print results to stdout. Use fast I/O methods to avoid bottlenecks.

4. Implement Operations

For PUT, insert or update the key-value pair. For GET, retrieve and print the value or a sentinel if absent. For DEL, remove the key if present. For COUNT, print the current number of keys.

5. Analyze Complexity

Explain that each operation is O(1) on average, so processing N commands takes O(N) overall. Mention worst-case scenarios and how to mitigate them.

Key Points to Mention

  • Hash map provides O(1) average-case time for PUT, GET, DEL, and COUNT.
  • Use buffered I/O to handle large input/output efficiently.
  • Handle edge cases: GET on missing key, DEL on missing key, updating existing key.
  • COUNT can be maintained as a separate variable or by using the map's size method.
  • Overall time complexity is O(N) for N commands, meeting the linear time requirement.
  • Consider memory usage and potential need for resizing the hash map.

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