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Openai·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
May 2026

Summary

Interviewed for an ML Engineer role at OpenAI and got a surprisingly algorithmic question about version dependency resolution. Not what I was expecting from an AI company but it made sense in retrospect given how much infra work these teams deal with.

Questions Asked (1)

Q1

You have a list of versioned dependencies (formatted as major.minor.patch) and a black-box API that tells you whether a given version supports a feature. The API is rate-limited, so you can't call it on every version. Support is not monotonic across versions, meaning a higher version might drop support, but some later version is guaranteed to restore it. Design an algorithm to find the earliest version that supports the feature while keeping API calls sub-linear.

Algorithms & Data StructuresTechnical Trade-offsAPI & Integrations
Author's notes

This one took me a minute to even parse.

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AI HintsAI Generated

Suggested Approach

Clarify that the problem is equivalent to finding the first true in a boolean array with one contiguous false-to-true transition, despite non-monotonicity elsewhere. Propose a two-phase algorithm: exponential search to find a version that supports the feature, then binary search between the last known unsupported and the found supported version to pinpoint the earliest support. Emphasize that this achieves O(log n) API calls and discuss handling edge cases like no support at all.

Pro tip: Mention that you would cache API responses and use a version comparison function that handles semantic versioning correctly (e.g., 1.10.0 > 1.9.0), showing attention to real-world implementation details.

1. Clarify the problem and constraints

Restate the problem: find the earliest version that supports the feature, given non-monotonic support but a guaranteed later restoration. Confirm that the API is a black box and rate-limited, so minimizing calls is critical.

2. Identify the monotonic segment

Explain that the guarantee of a later version restoring support implies there exists a contiguous range where support becomes true and stays true. The goal is to find the left boundary of this range.

3. Design a two-phase search

Use exponential search (doubling) to find a version that supports the feature, starting from the lowest version. Then binary search between the last unsupported version and the found supported version to find the earliest support.

4. Analyze complexity and edge cases

Show that the algorithm uses O(log n) API calls. Discuss edge cases: no version supports the feature, the first version supports it, and the feature is restored multiple times (but earliest is still found).

5. Discuss optimizations and practical considerations

Mention caching results, handling rate limits with backoff, and using a correct version comparison function. Optionally, discuss if the API can be queried in batches or if there's a way to reduce calls further.

Key Points to Mention

  • Exponential search to find an upper bound where support is true
  • Binary search on the monotonic segment to find the earliest true
  • Time complexity: O(log n) API calls, which is sub-linear
  • Handling non-monotonicity by relying on the guaranteed restoration
  • Edge cases: no support, support at first version, multiple restorations
  • Caching and rate-limit handling for practical implementation

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