tiktok

TikTok AI Engineer Case Interview: Multimodal Recommendation and Safety at Scale

This TikTok case interview simulates designing and iterating on a production-ready AI system for short‑video recommendations with integrated content safety. It mirrors real TikTok/ByteDance interview patterns: fast-paced, data-driven, system-and-ML hybrid, focused on tradeoffs, experimentation rigor, and execution at massive scale. Scenario prompt: - Design a personalized Home Feed ranking pipeline for short videos that serves a global, multilingual, multimodal audience. Integrate safety moderation so unsafe or age-inappropriate content is filtered before ranking. Optimize for user value (watch time quality, session depth) under strict latency and reliability constraints. Core focus areas TikTok interviewers probe: - Problem framing: define objectives, success metrics, and guardrails (e.g., high-quality watch time, creator fairness, safety recall). - Multistage ranking architecture: retrieval → filtering/safety → pre-ranking → ranking → re-ranking/diversification. Discuss two-tower retrieval with ANN (e.g., Faiss/ScaNN), candidate generation strategies, and a deep ranking model (e.g., MMoE/attention/DIN-like) over multimodal signals (text, audio, vision, user–item features). - Safety integration: on-path moderation (fast) vs asynchronous pipelines (thorough), ensemble strategies (keyword/image/audio models), thresholding, human-in-the-loop escalation, and fail-closed behavior. Discuss recall targets and latency budgets. - Data/infra realism: streaming ingestion (e.g., Kafka/Flink), feature store (freshness/TTL/point-in-time correctness), training data curation, negative sampling, feedback loops, online learning or warm-start, and embedding/version management. - Experimentation and metrics: A/B design, guardrails (safety incidents, creator health), primary/secondary metrics (video completion/long-view rate, qualified watch time, shares/follows, retention), counterfactual bias control, ramp/rollback criteria. - Globalization: language/region/age signals, device/network constraints, and content understanding across locales. - Performance/serving: p50/p95 latency targets (e.g., ~50 ms retrieval, ~100 ms ranking), QPS scaling, caching (user/item embeddings, top-K recall cache), quantization/distillation, GPU/CPU tradeoffs, autoscaling, resilience and fallback ranking. - Fairness/creator ecosystem: cold-start handling for new users and new creators, exploration vs exploitation (bandits/epsilon-greedy/TS), de-duplication/diversity. What you’ll do in the interview (typical 60 min): - 5 min: Clarify goals, metrics, constraints, and safety requirements. - 25–30 min: Propose end-to-end architecture and key models; justify tradeoffs with TikTok-like scale and latency in mind. - 10–15 min: Deep dive on one area (e.g., multimodal ranking model, feature store design, safety ensemble, ANN retrieval, or online learning/experimentation plan). - 5–10 min: Edge cases and follow-ups: spam/abuse waves, policy updates, traffic spikes, model regression, multilingual expansion, or on-device personalization. Constraints to respect (you set exact numbers, defend choices): - Latency budgets per stage; strict SLOs at p95. - Safety: near-zero tolerance; fail-safe defaults; measurable recall/precision; age gating. - Scale: high QPS with global distribution; multi-region reliability; graceful degradation. - Privacy/compliance: PII handling, logging, and access controls appropriate for a global product. Expected deliverables (verbal/whiteboard/doc): - Problem statement with measurable success criteria and safety guardrails. - System diagram and dataflow: retrieval/ranking, safety checkpoints, caches, feature freshness. - Model choices and training pipeline: features, labels, sampling, offline metrics vs online impact. - Experiment design and rollout: KPIs, guardrails, power, ramp/rollback plan, monitoring dashboards. - Risk analysis: feedback-loop bias, popularity bias, cold-start, adversarial content; mitigations. Evaluation rubric (how TikTok typically assesses): - Clarity and prioritization under time pressure; ability to make principled tradeoffs. - Technical depth across ML and infra; production realism at TikTok scale. - Metrics and experimentation rigor; safety integration that is practical and reliable. - Communication with structure; responsiveness to probing and counterfactuals. - Ownership mindset: iteration plan, debuggability, and how you’d land this in production. Interviewer follow-up probes you should anticipate: - How to raise safety recall without blowing latency/quality; where to place models in the stack. - Handling creator cold-start fairly while protecting user experience. - Degradation plans if the safety service is slow/down; ranking fallbacks. - Reducing inference cost via distillation/quantization/caching while maintaining quality. - Designing an A/B that isolates exploration policy changes from ranking changes.

engineering

60 minutes

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About This Interview

Interview Type

PRODUCT SENSE

Difficulty Level

4/5

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