Coinbase·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

Coinbase ML engineer interview, coding round focused on implementing a classic unsupervised learning algorithm from scratch. Nothing flashy but it required you to actually know the mechanics, not just name-drop the concept.

Questions Asked (1)

Implement K-Means clustering from scratch. A distance function is provided. Given a dataset and a value k, initialize k centroids, then repeatedly assign each point to its nearest centroid and recompute centroids as the mean of assigned points. Stop when assignments no longer change or you hit a max iteration limit. Return final assignments and centroids.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I knew k-means conceptually but writing it out cleanly under pressure is a different thing.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and assumptions (e.g., distance metric, data types, convergence criteria). Then outline the K-Means algorithm step-by-step, emphasizing initialization, assignment, update, and termination. Finally, discuss implementation details, edge cases, and potential optimizations.

Pro tip: Mention that K-Means is sensitive to initialization and suggest using K-Means++ for better convergence, but note that for this implementation, a simple random initialization is acceptable if you handle empty clusters.

1. Clarify Requirements and Assumptions

Confirm the distance function, data format, and expected output. Ask about handling empty clusters and whether to use random or smart initialization.

2. Outline the Algorithm

Describe the iterative process: initialize centroids, assign points to nearest centroid, recompute centroids, and check for convergence.

3. Detail Implementation Steps

Explain how to compute distances, assign clusters, update centroids, and track convergence. Mention data structures for efficiency.

4. Discuss Edge Cases and Optimizations

Address empty clusters, convergence criteria, and potential improvements like K-Means++ or vectorization.

5. Analyze Complexity and Trade-offs

State time and space complexity, and discuss trade-offs between different initialization methods and convergence thresholds.

Key Points to Mention

Initialization: random vs. K-Means++ and its impact on convergence.
Distance computation: using the provided distance function efficiently.
Assignment step: mapping each point to the nearest centroid.
Update step: recomputing centroids as the mean of assigned points.
Convergence: stopping when assignments don't change or max iterations reached.
Handling empty clusters: reassign or reinitialize centroids.
Complexity: O(n * k * d * iterations) time, O(n + k) space.
Potential optimizations: vectorization, using triangle inequality, or mini-batch K-Means.

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