Openai·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026Remote

Summary

Coding round at OpenAI for an ML Engineer role, one problem that looked like a simple binary search until it really wasn't. The time limit on the 'Interesting' test cases is brutal and a flat search just doesn't cut it.

Questions Asked (1)

You have a sorted list of semantic version strings and a function isWorking(version). Find any working version efficiently, knowing the version space is hierarchical (major.minor.patch) and each axis is monotone with respect to the working predicate. Some versions may be missing patch or minor components. Your solution must pass Simple, Large, and Interesting test case categories within the time limit.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went straight for binary search on the flat list and it passed Simple and Large.

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AI HintsAI Generated

Suggested Approach

Model the version space as a 3D grid where each axis (major, minor, patch) is monotone with respect to isWorking. Use a hierarchical binary search: first find the highest working major version, then within that major find the highest working minor, and finally the highest working patch. Handle missing components by treating them as 0 and ensure the search respects the monotonicity.

Pro tip: Clarify the monotonicity direction: if a version works, all lower versions on each axis also work (or vice versa). This determines whether you search for the boundary from working to non-working or the other way. Also, consider that missing components might imply defaults (e.g., missing patch means patch=0), so normalize versions before comparison.

1. Clarify monotonicity and normalization

Confirm with the interviewer whether the predicate is monotone increasing or decreasing along each axis, and how missing components are interpreted (e.g., '1.2' means '1.2.0'). Normalize all versions to major.minor.patch for consistent comparison.

2. Binary search on major version

Since the list is sorted, binary search for the highest major version that contains at least one working version. Use isWorking on the highest patch of each major to determine if that major has any working version.

3. Binary search on minor within the major

Within the identified major, binary search for the highest minor version that contains a working patch. Again, test the highest patch of each minor to check if that minor has any working version.

4. Binary search on patch within the minor

Within the identified minor, binary search for the highest working patch. This yields a working version. If the monotonicity is decreasing, adjust the search to find the lowest working version instead.

5. Handle edge cases and verify

Check if no working version exists (return null or appropriate value). Verify the found version is indeed working and that no higher working version exists by testing adjacent versions. Discuss time complexity: O(log M + log m + log p) where M, m, p are the number of majors, minors, patches respectively.

Key Points to Mention

Monotonicity assumption: if a version works, all versions with lower (or higher) major/minor/patch also work, enabling binary search.
Normalization of missing components: treat missing minor or patch as 0 for consistent comparison.
Hierarchical binary search: first on major, then minor, then patch, reducing the search space at each level.
Time complexity: O(log N) overall, where N is the total number of versions, but more precisely O(log M + log m + log p).
Edge cases: no working version, all versions working, missing components affecting the search boundaries.
Trade-offs: binary search requires random access; if the list is a linked list, consider other approaches. Also, if isWorking is expensive, minimize calls.

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