Pinterest AI Engineer Case Interview — Multimodal Discovery, Recommendations, and Safety

This case simulates a real Pinterest problem where you design and evaluate an ML system that improves inspiration quality on core discovery surfaces (Homefeed, Related Pins, Search/Lens, and Shopping). Interviewers expect you to think Pinner-first, balance inspiration with safety, and connect model choices to business and well-being outcomes. Scenario prompt used in the interview: "For new and returning Pinners, increase high-quality saves and relevant engagement on Homefeed while reducing low-quality or unsafe content exposure. Propose an end-to-end approach covering data, modeling, online serving, and experimentation. Account for cold-start Pins/Creators, internationalization, and shopping intent." What strong answers cover at Pinterest: 1) Problem framing and goals (5–10 min) - Define Pinner-centric objectives (e.g., quality saves per session, relevant pin clicks, downstream board organization, healthy session depth) and business-aligned outcomes (shopping intent satisfaction, advertiser-friendly context) with clear trade-offs. - Identify guardrails (policy violation rate, diversity/novelty, creator fairness, latency/cost) and discuss short- vs long-term metrics. 2) Signals and data (10 min) - Multimodal features: image embeddings, text/title/alt-text embeddings, link/domain quality signals, board and co-occurrence graphs, topic annotations, locale and language, session context, lightweight personalization profiles. - Content health/quality: spam/safety scores, deduplication, freshness/seasonality, shopping catalog metadata (price/availability/merchant quality). 3) Candidate generation and ranking (20 min) - Retrieval: combine sources such as graph-based board co-occurrence, visual similarity, semantic/text retrieval, and personalized nearest-neighbor over learned Pinner interest vectors. - Ranking: a two-stage or three-stage stack (lightweight pre-rank, neural ranker, re-ranker) that blends relevance, inspiration quality, diversity, and safety. Discuss training data, label definitions (e.g., saves vs shallow clicks), negative sampling, and handling feedback loops. - Cold start: creator/page-level priors, content understanding via vision+language models, few-shot generalization, and exploration policies. - Safety and integrity by design: pre- and post-rank safety filters, calibrated thresholds per market, appeals/override pathways, and explainability for policy audits. 4) Online serving and system design (10 min) - Low-latency retrieval with ANN, feature store for real-time and batch features, streaming updates for fresh pins and session signals, canarying and gradual rollout across markets and languages. - Diversity, deduping, and personalization at request time; fallbacks for sparse contexts and network failures. 5) Experimentation and evaluation (10 min) - A/B design with cohorting (new vs tenured Pinners, country/locale), holdouts for long-term effects, and success criteria gates (metric lifts with guardrails not regressing). - Offline/online alignment: offline proxies, counterfactual evaluation, and plan for post-launch monitoring (drift, safety incidents, creator impact). What interviewers assess: - Product and Pinner sense (does the solution feel inspirational and useful, not just optimized for clicks?). - ML depth in multimodal representation learning, retrieval/ranking, safety modeling, and exploration. - Practical systems thinking (latency, cost, reliability, rollout strategy). - Experimentation rigor and metric design aligned to Pinterest’s mission and surfaces. - Clear, collaborative communication and trade-off reasoning reflective of Pinterest’s culture.