Coinbase·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Coinbase ML Engineer interview with a coding question that looks straightforward but has some edge case nuance if you're not careful about the boundary conditions.

Questions Asked (1)

Given an array of numbers, find all local maxima, where a local maximum is an element strictly greater than both its immediate neighbors. Treat out-of-bounds positions as negative infinity. Return the indices or values of all such elements.

Algorithms & Data Structures

Author's notes

The core logic is simple enough but I almost fumbled the boundary handling.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose a single-pass O(n) solution that checks each element against its neighbors, treating out-of-bounds as negative infinity. Discuss trade-offs and potential optimizations, and relate the problem to ML contexts like peak detection in signals or feature extraction.

Pro tip: Mention that treating out-of-bounds as negative infinity simplifies edge handling and ensures endpoints can be local maxima if they exceed their only neighbor. Also, explicitly state whether you return indices or values, as ambiguity can cost points.

1. Clarify requirements and edge cases

Ask whether to return indices or values, and confirm handling of empty arrays, single-element arrays, and plateaus (equal neighbors).

2. Outline the brute-force approach

Explain that a naive solution checks each element against its neighbors, which is O(n) time and O(1) space, but may be inefficient if done with explicit boundary checks.

3. Propose an optimized single-pass solution

Use a loop with sentinel values (negative infinity) for out-of-bounds, comparing each element to its left and right neighbors to identify local maxima in one pass.

4. Analyze complexity and trade-offs

State that the solution runs in O(n) time and O(1) extra space, and discuss whether returning indices or values affects memory.

5. Relate to ML engineering at Coinbase

Connect the problem to real-world ML tasks like detecting peaks in time-series data (e.g., crypto price anomalies) or feature extraction, showing practical relevance.

Key Points to Mention

Time and space complexity: O(n) time, O(1) space for the optimal solution.
Edge cases: empty array, single element, all equal elements, and strictly increasing/decreasing arrays.
Definition of local maximum: strictly greater than both neighbors; out-of-bounds treated as negative infinity.
Choice of returning indices vs. values and how that affects implementation.
Potential for parallelization or vectorization in ML contexts (e.g., using NumPy).
Handling of plateaus: equal neighbors mean no local maximum, which may require clarification.

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