Spotify AI Engineer Case Interview: Large‑Scale Personalization and Experimentation for Home/Discover

This case mirrors real Spotify interviews that emphasize pragmatic ML system design, experimentation rigor, and collaboration within a squad. You will design an end‑to‑end AI solution to improve Home recommendations and the "Discover Weekly"-style experience for both Premium and Ad‑Supported users. What the interviewer looks for: - Clear problem framing tied to Spotify’s business: differentiate Premium vs Ad‑Supported objectives (e.g., retention and satisfaction vs engagement that also respects ad delivery and latency budgets). State success metrics and guardrails up front. - Data and signals: implicit feedback (plays, skips, replays, dwell, follows, additions to library), contextual/device signals, audio and text embeddings, podcast signals, and cold‑start strategies for new users and new content. Call out data minimization and regional/age constraints (e.g., GDPR). - Retrieval and ranking at Spotify scale: propose candidate generators (collaborative filtering plus content‑based) and an ANN layer (e.g., using Spotify’s open‑sourced Annoy) for fast retrieval; design a ranking stack with a lightweight online ranker and optional on‑device reranker for privacy and latency. Discuss diversity/novelty/freshness re‑ranking and creator/catalog fairness. - System design details: offline training and feature pipelines (batch/stream via a workflow orchestrator such as Luigi or equivalent), online feature stores/caches, model registry, blue/green or shadow deployments, fallbacks when models or features are stale, and p99 latency/availability targets appropriate for Spotify’s global footprint. - Experimentation plan: A/B design with primary KPIs (e.g., playlist saves, long‑session starts, completion rate, downstream retention proxies), guardrails (skip rate, ad revenue impact, catalog diversity, crash/ANR rate, infra cost), power/ramp strategy, and how to avoid sequential peeking. Outline diagnostics for heterogeneous treatment effects across markets and platforms. - Responsible AI and safety: bias audits (popularity and geography skew), popularity tail protection, adversarial/bot‑play detection, privacy by design, transparency to creators, and age‑appropriate recommendations. - Execution and iteration: how you would ship a v1 with a "think it, build it, ship it, tweak it" mindset, partner with PM/Design/Data Science/Legal within a squad, and define a 30/60/90‑day roadmap. Format inside the interview (guide): - 10 min: problem framing and metrics - 20 min: data, modeling, retrieval/ranking, and architecture (whiteboarding encouraged) - 15 min: experimentation and rollout - 10 min: responsible AI, risks, and edge cases - 5 min: extensions (podcasts/audiobooks, multilingual, on‑device) and Q&A Deliverables you should articulate: a metric tree, a high‑level architecture for offline/online flows, a candidate + ranker design with diversity/risk controls, and an A/B plan with success/stop criteria.