Openai·Machine Learning Engineer·Onsite - Coding / Algorithms·Senior

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Apr 2026

Summary

Coding round for an ML Engineer role at OpenAI, 75 minutes, Python environment issue ate into the time a bit. The problem had three escalating test cases that pushed from a simple linear scan all the way to a hierarchical binary search, plus a verbal follow-up I fumbled through.

Questions Asked (1)

Given a sorted structure, solve a search problem that requires progressively more efficient approaches: first a linear scan, then a global binary search, then a hierarchical binary search across nested levels.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The first two parts went fine, linear scan to binary search is pretty standard muscle memory at this point.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the structure and search problem, then present three solutions in increasing efficiency: linear scan, global binary search, and hierarchical binary search. For each, explain the algorithm, analyze time/space complexity, and discuss trade-offs. Finally, connect to ML engineering contexts like searching sorted embeddings or model parameters.

Pro tip: Emphasize that hierarchical binary search reduces complexity from O(log N) to O(log N + log M) when the structure has nested levels, and relate it to real-world ML systems like hierarchical indexes in vector databases.

1. Clarify the problem and structure

Ask questions to understand the sorted structure (e.g., array, nested arrays, tree) and the search target. Confirm assumptions like sorted order and access patterns.

2. Present linear scan

Describe a simple linear scan, its O(n) time complexity, and when it might be acceptable (e.g., small n or unsorted data).

3. Present global binary search

Explain standard binary search on the entire sorted structure, achieving O(log n) time. Discuss its limitations with nested or hierarchical data.

4. Present hierarchical binary search

Describe how to perform binary search at each level of the hierarchy, reducing search space progressively. Analyze complexity as O(log n + log m + ...) and compare to global binary search.

5. Discuss trade-offs and ML applications

Compare the approaches in terms of time, space, and implementation complexity. Relate to ML scenarios like searching sorted embeddings, hyperparameter tuning, or hierarchical indexes.

Key Points to Mention

Time and space complexity analysis for each approach
Assumptions about the sorted structure and access patterns
Trade-offs between simplicity and efficiency
Edge cases: empty structure, target not found, duplicates
Real-world ML applications: vector databases, model parameter search, hyperparameter tuning
Potential optimizations: interpolation search, exponential search, or using tree structures

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