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Microsoft·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
Apr 2026

Summary

Microsoft ML Engineer interview where they asked me to implement K-Means from scratch. Pretty deep technical question, they wanted to see the full algorithm not just pseudocode.

Questions Asked (1)

Q1

Implement K-Means clustering from scratch. Given a 2D array of points and a value K, return the cluster assignment for each point and the final centroids. Cover initialization (random or k-means++), the assignment step using Euclidean distance, centroid updates, and convergence checking. Python, NumPy, or PyTorch are all fine.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

I started with random init because k-means++ felt like it would take too long to explain under pressure, and I think that was the right call.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then outline the K-Means algorithm steps: initialization (random or k-means++), assignment, update, and convergence. Implement each step in a clean, vectorized manner using NumPy, and test with a small example to ensure correctness.

Pro tip: Mention that k-means++ initialization often leads to faster convergence and better clusters, and that using vectorized operations (e.g., broadcasting) is crucial for performance in production.

1. Clarify and Plan

Ask clarifying questions about input format, distance metric, convergence criteria, and initialization method. Outline the algorithm steps and choose an implementation approach (e.g., NumPy).

2. Initialize Centroids

Implement either random initialization (select K random points) or k-means++ (probabilistic selection based on distance). Explain the trade-offs and why k-means++ is often preferred.

3. Assignment Step

For each point, compute Euclidean distance to all centroids and assign it to the nearest one. Use vectorized operations for efficiency.

4. Update Step

Recompute each centroid as the mean of all points assigned to it. Handle empty clusters by reinitializing or keeping the previous centroid.

5. Convergence Check

Check if assignments or centroids have changed (within a tolerance). If not converged and max iterations not reached, repeat from step 3.

Key Points to Mention

  • Choice of initialization: random vs. k-means++ and its impact on convergence and final clusters.
  • Euclidean distance computation and vectorization for performance.
  • Handling empty clusters: strategies like reinitializing to a random point or farthest point.
  • Convergence criteria: assignment changes, centroid movement, or objective function (inertia) threshold.
  • Time complexity: O(n * K * d * iterations) and potential optimizations (e.g., using tree-based methods).
  • Evaluation metrics: inertia, silhouette score, and how to choose K (elbow method).

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