I started with a two-tower model and ANN index, which felt right, but I fumbled when they pushed on how retrieval feeds into ranking.
Start by clarifying requirements and scale, then design the query-to-video retrieval component as a two-tower model with approximate nearest neighbor search, and finally explain how it integrates into a multi-stage recommender stack (retrieval, ranking, re-ranking). Emphasize trade-offs between latency, accuracy, and diversity at each stage.
Pro tip: Show awareness of Snapchat's unique constraints: short-form, ephemeral content, and mobile-first low-latency inference. Mention how you'd handle cold-start and multimodal signals (text, audio, visual) in retrieval.
Ask about scale (DAU, QPS), latency budget, content types (short videos), and business goals (engagement, diversity). Confirm the need for end-to-end design from query to video retrieval.
Propose a two-tower architecture: query encoder (text, user context) and video encoder (multimodal features). Use approximate nearest neighbor (ANN) search for efficient retrieval. Discuss training with contrastive loss and hard negatives.
Explain how retrieval feeds into ranking (e.g., deep learning ranker with user-video features) and re-ranking (diversity, freshness, business rules). Highlight the funnel: retrieval narrows millions to thousands, ranking to hundreds, re-ranking to tens.
Discuss trade-offs: latency vs. accuracy, model complexity vs. maintainability, and offline vs. online metrics. Propose A/B testing and metrics like CTR, watch time, and diversity.
Recap the end-to-end flow and suggest potential improvements like real-time updates, multi-task learning, or reinforcement learning for re-ranking.
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Start by outlining a multi-modal architecture that combines visual, audio, and text features, then address the 80% missing text by proposing robust fallback strategies like self-supervised learning and cross-modal alignment. Emphasize trade-offs between complexity, scalability, and performance, and tie your answer to Snapchat's use cases like recommendation and search.
Pro tip: Mention that you would first quantify the impact of missing text on downstream tasks to prioritize investment in fallback methods, showing a data-driven and pragmatic approach. Also, highlight that leveraging unlabeled videos through self-supervision can turn the 'missing text' problem into an opportunity for better generalization.
Ask about the specific task (e.g., classification, retrieval), scale of data, latency requirements, and existing infrastructure to tailor your solution.
Propose extracting visual features (e.g., CNN/Transformer on frames), audio features (e.g., spectrograms), and text features (e.g., BERT) when available, and fusing them via attention or concatenation.
For the 80% without reliable text, suggest using self-supervised objectives (e.g., contrastive learning between modalities), pseudo-labeling, or generating synthetic text via captioning models.
Define metrics for both with-text and without-text subsets, and propose A/B testing to measure impact on business metrics like engagement.
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Covered contrastive training offline, FAISS or ScaNN for ANN, and a rough sketch of how embeddings get refreshed.
Start by clarifying the retrieval system's requirements (e.g., scale, latency, freshness) and then walk through each component—offline training, online serving, and ANN indexing—explaining your choices and the trade-offs involved. Emphasize how your decisions align with Snapchat's needs, such as low-latency serving for a massive user base and frequent model updates.
Pro tip: Quantify the trade-offs with concrete numbers (e.g., 'Using HNSW over IVF reduced latency by 30% at the cost of 2x memory') to demonstrate practical experience and maturity.
Ask about scale (number of users, items), latency SLAs, update frequency, and hardware constraints to tailor your choices.
Discuss model architecture (e.g., two-tower), training data pipeline, frequency of retraining, and how you handle cold-start and feedback loops.
Explain how you serve embeddings and retrieve candidates in real-time, including caching, batching, and fallback strategies to meet latency targets.
Compare ANN algorithms (e.g., HNSW, IVF, PQ) and justify your choice based on recall, latency, memory, and update requirements.
Summarize key trade-offs (e.g., accuracy vs. speed, cost vs. freshness) and how you would monitor and iterate on the system.
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Went with recall at K and MRR, then mentioned offline vs online eval.
Start by defining what 'quality' means for a retrieval system in the context of Snapchat's products, such as relevance, diversity, and user engagement. Then describe a multi-layered evaluation strategy that combines offline metrics, online A/B testing, and product analytics to measure both retrieval accuracy and business impact. Emphasize how you would iterate based on these evaluations to improve the system.
Pro tip: Highlight the importance of aligning retrieval metrics with business goals and user satisfaction, and mention how you would use counterfactual evaluation or interleaving to quickly test new retrieval algorithms without full deployment.
Identify and prioritize metrics that reflect retrieval quality, such as precision@k, recall, NDCG, diversity, and coverage, tailored to Snapchat's use cases like Discover or Search.
Use historical data and labeled datasets to compute offline metrics, and perform error analysis to understand failure modes and potential biases.
Design and run controlled experiments to measure the impact of retrieval changes on key user engagement metrics (e.g., CTR, time spent, retention) and guardrail metrics.
Analyze user interaction logs and feedback to detect issues not captured by offline metrics, and use qualitative insights to inform improvements.
Continuously monitor performance, set up alerts for degradation, and iterate on the retrieval system based on evaluation results and business needs.
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Honestly the hardest part of the whole interview.
Start by defining popularity bias and its impact on retrieval and recommendation, then propose a multi-faceted solution that combines algorithmic adjustments, data curation, and evaluation metrics. Emphasize the need to balance bias reduction with relevance and engagement through experimentation and iterative refinement.
Pro tip: Frame your answer around a specific metric or experiment you'd run, showing you understand Snapchat's unique content ecosystem (e.g., ephemeral content, friend-based graph). Highlight that reducing bias is not just about fairness but also about long-term user retention and discovery.
Analyze current retrieval and recommendation outputs to measure popularity bias using metrics like coverage, Gini coefficient, or exposure disparity across content popularity tiers.
Implement techniques such as inverse propensity scoring, popularity debiasing in embeddings, or diversity-aware re-ranking to reduce overexposure of popular items while maintaining relevance.
Introduce exploration mechanisms (e.g., epsilon-greedy, Thompson sampling) and leverage user-specific signals to surface niche content that aligns with individual preferences.
Use offline metrics (e.g., recall@k, diversity) and online A/B tests measuring engagement (CTR, watch time) and bias reduction (exposure fairness) to validate trade-offs.
Continuously monitor for bias drift, gather user feedback, and refine algorithms to maintain a balance between relevance, engagement, and fairness.
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