Snap (Snapchat) AI Engineer Case Interview – Real‑time AR/ML Product Design and Deployment

This case simulates how an AI Engineer at Snap would scope, design, and ship an AI-powered, camera-first feature for Snapchat. You will choose one of two prompts and drive the discussion end-to-end, emphasizing Snap’s culture of being kind, smart, and creative, privacy-by-design principles, and shipping delightful, real-time AR experiences under strict latency and battery constraints. Prompts (interviewer picks one): (A) On-device AR Try-On Lens powered by segmentation/face-tracking and a lightweight generative model to personalize effects; or (B) Spotlight content quality and safety ranking using a multimodal model (video, audio, text) with real-time inference and creator-friendly appeal. What we expect you to cover: • Problem framing and success metrics tied to Snap behaviors (e.g., Lens activation rate, playtime, share rate, downstream retention; for ranking: watch time, completion rate, creator satisfaction, safety violations) and ML metrics (e.g., IoU, landmark tracking error, latency p95, model size). • System and model design: model choices suitable for mobile/near-real-time (e.g., quantized CNNs/transformers, distillation, pruning), streaming inference, GPU/CPU/Neural Engine delegation, platform constraints (iOS/Android), and integration with Lens Studio or creative tooling. • Data strategy: bootstrapping datasets without storing PII, synthetic data generation, labeling strategy, handling bias/fairness, and continuous data hygiene consistent with ephemeral-by-default principles. • Performance engineering: achieving 30+ FPS camera experience, p95 end-to-end latency budgets (e.g., <20–30 ms/frame on mid-tier devices), memory/thermal limits, fallback paths for older devices, and progressive rollout. • Evaluation and experimentation: offline eval, shadow/ghost mode, A/B testing, guardrails to prevent regressions in creation/sharing, and interpreting trade-offs between engagement, safety, and integrity. • Reliability and productization: model versioning, rollback plans, on-device update strategy, privacy review, abuse/safety defenses, and creator tooling feedback loops. • Collaboration and communication: how you’d partner with design, product, policy, and creator ecosystem to ensure the feature is fun, safe, and brand-aligned. Format: 5 min clarifying questions; 20–25 min solution walkthrough (architecture diagrams verbally), 15–20 min deep dives (latency, privacy, safety, failure modes), 10 min metrics/experimentation plan, 5 min risks and next steps, and 5 min Q&A. Deliverables during the case: a crisp problem statement, prioritized requirements, a sketched architecture, explicit latency/size targets, an evaluation plan with launch criteria, and trade-off discussion reflecting Snap’s camera-first, privacy-conscious, and creativity-forward ethos.