Google AI Engineer Case Interview — End‑to‑End ML System Design and Launch

Purpose: Assess role-related knowledge (RRK) in applied ML, general cognitive ability (GCA), and Googleyness/leadership behaviors through a structured, metrics-driven product/engineering case. Case theme (interviewer picks one, candidate may clarify/scope): - Abuse/quality detection at scale (e.g., spam/toxic content) for a global product with billions of events/day. - Ranking/recommendation for a large surface (e.g., feed or search results) with strict latency and fairness constraints. - On-device intelligent feature (e.g., smart replies) requiring privacy-preserving learning and limited compute. What the interview covers at Google: 1) Problem framing and success metrics (10–12 min) - Translate a vague product ask into an ML formulation; articulate objective(s), constraints, and trade-offs. - Define north-star and guardrail metrics: user-centric (retention, satisfaction), model-centric (precision/recall/PR AUC, calibration), and system-centric (P50/P95 latency, throughput, cost). - Discuss fairness/safety metrics, privacy considerations, and abuse/failure modes. 2) Data and feature strategy (8–10 min) - Identify signals, labels, sampling, and offline/online data splits; address leakage and skew. - Feature store/embedding strategy; handling missing/late data; real-time vs batch features; drift detection. 3) Modeling approach (8–10 min) - Establish baselines; justify classical vs deep models; retrieval + ranking stacks; use of pre-trained embeddings/LLMs. - Explain objective functions, class imbalance tactics, and interpretability requirements. - Highlight privacy-preserving and responsible AI choices (e.g., differential privacy at a high level, anonymization, bias checks). 4) Production ML system design (15–18 min) - High-level architecture: data ingestion, training pipeline, evaluation, feature store, online inference, caching, rollouts. - SLOs and reliability: error budgets, autoscaling, circuit breakers; blue/green or canary releases; rollback plan. - Hardware/infra trade-offs: CPUs vs GPUs/TPUs; batch vs streaming; cost/performance optimizations. 5) Experimentation and launch (6–8 min) - Offline evaluation vs online A/B; experiment design, power, ramp, and stop criteria; metric guardrails. - Post-launch monitoring: model/data drift, incident response, retraining cadence, and ethics review checkpoints. 6) Communication & collaboration (throughout) - Clear structure, crisp assumptions, and user-first reasoning; invites feedback and navigates ambiguity. Expected candidate deliverables (within the session): - A structured problem statement with explicit assumptions and success metrics. - An end-to-end diagram or stepwise walkthrough of the ML system. - A prioritized plan for experiments and a responsible AI checkpoint list. Evaluation rubric (Google-style signals, 1–4 scale per area; bar requires strong across most): - RRK: Applied ML depth and practicality; correct trade-offs; baseline-first thinking. - GCA: Decomposition, abstraction, and data-driven decision making under ambiguity. - System design (ML): Scalable, reliable, low-latency architecture with clear SLOs and rollback. - Data & experimentation: Sound evaluation design; awareness of bias, drift, and leakage. - Communication & Googleyness: User focus, collaboration, humility, and ethical judgment. Interviewer guidance (how it’s typically run): - Timebox to ~60 minutes; prompt is intentionally under-specified. - Encouraged probing: "What metric would you ship on?" "How does this fail?" "What would you cut for a v1?" - Depth over breadth; reward principled trade-offs and privacy/safety mindfulness. Candidate tips aligned to Google’s culture: - Put users first; justify metrics with user value. - Aim for simple, testable baselines before complex models; measure, then iterate (launch & learn). - Make responsible AI explicit: fairness checks, privacy-by-design, red-teaming high-risk failures. Anti-patterns (signals of concern): - Jumping to complex models without a baseline or metric definition. - Ignoring latency/SLOs, privacy, or fairness; no rollback/monitoring plan. - Hand-wavy experimentation without power/guardrails or unclear success criteria. Materials: Shared doc/whiteboard; diagrams and pseudocode are acceptable; no heavy coding expected.