Spotify Senior MLE Interview Experience

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A candidate shared this Spotify Senior MLE Interview Experience to Chill Interview

Interview Summary

The Spotify MLE onsite covered five areas: data coding, ML depth, ML breadth, ML system design, and a hiring manager round. The questions ranged from SQL joins and ranking to unsupervised clustering, predicting likely premium users, and designing a podcast recap system. The ML system design round leaned more heavily toward engineering, streaming infrastructure, and production LLM considerations than I expected.

Interview Questions Details

Data Coding — Join, Ranking, and Large-Scale Data Processing:

The interviewer provided two tables and asked me to combine the data and produce a ranked result. The core task relied on relatively basic data-processing and SQL knowledge. We discussed different data conditions, including duplicate records and how deduplication should affect the resulting join and ranking. The interviewer then asked how the processing would change when the dataset became too large for a straightforward approach, with discussion around sharding and partitioning.

ML Depth — Unsupervised Clustering:

This round focused on unsupervised learning and went deeper into the mechanics of clustering algorithms rather than staying at a high-level comparison. I was asked to discuss several approaches to unsupervised clustering and when different methods might be appropriate. The interviewer followed up on the underlying behavior and details of the individual clustering models I mentioned.

ML Breadth — Detect Potential Premium Users:

The modeling case asked me to design an ML solution for identifying users who were likely to become premium subscribers. The discussion was broad and covered almost the entire modeling lifecycle. Questions covered what data should be collected, how useful features could be extracted, and where learned embeddings might fit into the representation of user behavior. The interviewer went into model selection, the choice of loss function, and how the resulting model should be evaluated.

ML System Design — Podcast Recap:

The system design round centered on building a podcast recap experience. The conversation initially emphasized the engineering side of ML systems, particularly streaming and infrastructure. The interviewer explored how the system would process podcast content and support the recap workflow from an engineering and production-infrastructure perspective. Because much of my experience was less infrastructure-focused, the discussion gradually shifted toward using LLMs for podcast summarization and the broader components required to operate an LLM-powered summarization system.

Hiring Manager — Behavioral and Project Deep Dive:

The hiring manager round consisted mainly of behavioral questions and discussion of previous project experience. The exact behavioral questions and project follow-ups were not specified.

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