Spotify interview questions
Big tech · 2 questions reported
- junior 0
- mid 2
- senior 0
These are 2 technical questions candidates reported being asked in Spotify interviews for AI engineer, ML engineer and applied AI engineer roles, each with a model answer you can say out loud in a minute or two. They cover training and ml system design: 0 junior, 2 mid-level and 0 senior questions. Every question comes from a public interview write-up that names Spotify.Updated
Spotify training and optimisation questions
Model answer
Choose by the cluster shape you expect and data size.
K-means minimises within-cluster squared distance, assumes compact similar-size clusters, and needs k. Hierarchical merges by a linkage rule into a dendrogram, suiting small, nested data. DBSCAN grows dense regions and flags outliers, finding any shape but struggling with varying density. Gaussian mixtures fit soft, elliptical clusters by EM. Spectral clustering uses eigenvectors of a similarity graph, handling non-convex shapes on moderate data.
Spotify mL system design questions
Model answer
Score all 10,000 candidates with a fast pointwise model of user, context and track, keep the top hundred or so. Then build the list slot by slot: a sequence-aware model scores the remaining tracks given the ones already chosen, for flow in tempo, energy and genre.
Apply diversity constraints greedily, MMR-style, such as at most two tracks per artist. Evaluate offline on skips and completions, then A/B test listening time and skip rate.
Each question comes from a public interview write-up or video that names Spotify; the answers are ours. Not affiliated with Spotify.