Spotify·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Spotify ML engineer interview that went deep on unsupervised learning. The whole session was basically one long question about clustering methods, which sounds manageable until you realize how many angles they want you to cover.

Questions Asked (1)

Walk through multiple unsupervised clustering approaches and compare them across dimensions like core intuition, cluster shape assumptions, hyperparameter selection, scalability, failure modes, evaluation without labels, and preprocessing for high-dimensional or sparse data.

Technical Trade-offsAlgorithms & Data StructuresSystem Design

Author's notes

This was one question but it kept branching.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Structure your answer by first grouping clustering algorithms into families (centroid-based, density-based, hierarchical, and model-based), then compare them across the requested dimensions using a consistent framework. Use a concrete example like Spotify's music recommendation or user segmentation to ground the trade-offs, and emphasize how you would choose an approach based on data characteristics and business goals.

Pro tip: Always tie the comparison back to the evaluation challenge: since labels are rare, mention how you'd use internal metrics (silhouette, Davies-Bouldin) combined with domain-specific validation (e.g., A/B tests for playlist clustering). This shows you understand that clustering is often a means to an end, not the final goal.

1. Categorize algorithms by family

Briefly introduce the main families: centroid-based (K-means, K-medoids), density-based (DBSCAN, HDBSCAN), hierarchical (agglomerative, divisive), and model-based (Gaussian Mixture Models, spectral clustering). This sets the stage for structured comparison.

2. Compare core intuition and cluster shape assumptions

For each family, explain the underlying intuition (e.g., centroids minimize variance, density connects high-density regions) and the shapes they assume (spherical, arbitrary, nested). Highlight how these assumptions affect performance on real-world data.

3. Discuss hyperparameter selection and scalability

Detail key hyperparameters (e.g., k for K-means, eps and minPts for DBSCAN, linkage for hierarchical) and methods to select them (elbow, silhouette, domain knowledge). Compare computational complexity and scalability to large datasets, noting which algorithms can handle millions of points.

4. Address failure modes and evaluation without labels

Identify common failure modes (e.g., K-means fails on non-convex clusters, DBSCAN struggles with varying densities). Explain internal evaluation metrics (silhouette, Calinski-Harabasz) and how to validate clusters using domain-specific proxies or downstream tasks.

5. Cover preprocessing for high-dimensional or sparse data

Discuss the curse of dimensionality and techniques like PCA, UMAP, or feature hashing for dimensionality reduction. For sparse data (e.g., text), mention using cosine distance or specialized algorithms like spherical K-means, and the importance of scaling.

Key Points to Mention

K-means: assumes spherical clusters, requires specifying k, scales well with MiniBatchKMeans, but sensitive to outliers and initialization.
DBSCAN/HDBSCAN: finds arbitrary shapes, automatically determines number of clusters, but struggles with varying densities and high-dimensional data.
Hierarchical clustering: no need to specify k, provides dendrogram for interpretation, but O(n^2) or O(n^3) complexity limits scalability.
Gaussian Mixture Models: probabilistic, allows soft assignments, can model elliptical clusters, but assumes Gaussian distributions and can overfit.
Evaluation without labels: use silhouette score, Davies-Bouldin index, and domain-specific validation like stability analysis or A/B testing.
Preprocessing: standardize features, apply dimensionality reduction (PCA, UMAP) for high-dimensional data, and use cosine distance for sparse data.

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