Apple·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Apple ML Engineer technical screen, basically one big coding question the whole time. They wanted a full K-Means implementation from scratch, no shortcuts, and expected you to talk through the messy parts too.

Questions Asked (1)

Implement K-Means clustering from scratch in Python using only NumPy. Given a 2D array of shape (n, d) and an integer k, return the final centroids and a label array. Cover initialization, the assignment and update loop, convergence, and how you'd handle edge cases like empty clusters.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went with k-means++ initialization because I figured random init would invite a follow-up about why I didn't.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and assumptions, then outline the K-Means algorithm step by step, emphasizing vectorized NumPy operations for efficiency. Discuss initialization strategies, convergence criteria, and edge cases like empty clusters, and finally provide a clean implementation with comments.

Pro tip: Mention that you would use K-Means++ initialization to improve convergence and avoid poor local minima, and that you'd handle empty clusters by reassigning them to the point farthest from its centroid. This shows practical experience beyond the basics.

1. Clarify requirements and assumptions

Confirm input shapes, data types, and whether to return labels as integers. Discuss distance metric (Euclidean) and convergence tolerance.

2. Initialize centroids

Choose an initialization method: random selection from data points or K-Means++ for better spread. Explain the trade-offs.

3. Implement assignment and update loop

Vectorize distance computation using NumPy broadcasting to assign each point to the nearest centroid, then update centroids as the mean of assigned points.

4. Define convergence and handle edge cases

Stop when centroids shift less than a tolerance or max iterations reached. Handle empty clusters by reassigning them to the farthest point from its centroid.

5. Return results and discuss complexity

Return final centroids and labels. Mention time complexity O(n*k*d*iterations) and potential optimizations like using squared distances to avoid sqrt.

Key Points to Mention

Vectorized distance computation using NumPy broadcasting (e.g., using np.linalg.norm or squared distances).
K-Means++ initialization for better centroid seeding and faster convergence.
Convergence criteria: centroid movement below tolerance or max iterations.
Handling empty clusters by reassigning to the point farthest from its centroid.
Time complexity and scalability considerations for large datasets.
Potential improvements: using Mini-Batch K-Means or Elkan's algorithm for efficiency.

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