This was the main question and it ate up most of the session.
Start by clarifying the product goals and success metrics for AR lens recommendations, then walk through the ML system design in a logical flow: candidate generation, ranking, training data, and online serving. Emphasize trade-offs between relevance, diversity, and latency, and tie technical choices back to user engagement and business impact.
Pro tip: Anchor your answer in Snapchat's unique AR ecosystem—lenses are creative, ephemeral, and socially driven—so highlight how you'd leverage signals like friend usage, camera context, and trending lenses to drive recommendations. Also, proactively discuss cold-start and freshness, since new lenses are constantly added.
Define what success means: e.g., increase lens usage, retention, or sharing. Identify key metrics like CTR, lens apply rate, and session length, and consider constraints like latency and diversity.
Outline multiple sources: collaborative filtering (user-user, item-item), content-based (visual features, tags), trending/popular, and social graph (friends' usage). Ensure a manageable candidate set for ranking.
Choose a model (e.g., two-tower or gradient-boosted trees) to score candidates. Incorporate features like user history, lens metadata, context (time, location), and social signals. Optimize for multiple objectives (engagement, diversity).
Describe data collection: implicit feedback (views, applies, shares) and explicit (likes). Address negative sampling, label definition, and handling biases. Discuss offline evaluation and online A/B testing.
Explain the serving flow: retrieve candidates, rank in real-time, apply business rules (e.g., diversity, freshness), and return top-K. Discuss latency requirements, caching, and fallback strategies.
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Start by clarifying that cold start is a classic exploration-exploitation trade-off, and that the solution depends on the type of cold start (new lens vs. new user) and the available data. Then outline a multi-pronged strategy: for new lenses, leverage content-based features and contextual bandits; for new users, use demographic/contextual signals and rapid online learning. Finally, discuss how to measure success and iterate.
Pro tip: Emphasize that cold start is not a one-time problem but an ongoing process—new lenses and users arrive continuously, so you need a system that adapts quickly and balances exploration with exploitation. Also, mention that at Snapchat, the cost of a bad recommendation is low (a swipe), so you can afford more exploration than in other domains.
Define what 'cold start' means for new lenses (no interaction data) and new users (no historical behavior). Ask about the scale, latency requirements, and available signals (e.g., lens metadata, user demographics, context).
Use lens metadata (e.g., category, visual features, creator) to compute content-based similarities and recommend to users with matching preferences. Combine with contextual bandits to explore new lenses efficiently.
For new users, rely on demographics, device, time, and location to make initial recommendations. Employ online learning (e.g., Thompson sampling) to quickly update preferences from early interactions.
Integrate both cases into a single system that balances exploration and exploitation, e.g., using a multi-armed bandit framework with features from both lenses and users. Ensure the system can handle both cold-start scenarios simultaneously.
Define metrics (e.g., engagement, retention, diversity) and set up A/B tests to evaluate the cold-start strategy. Monitor performance and adjust exploration rates based on feedback.
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I knew this conceptually but explaining it cleanly under pressure was harder than expected.
Start by explaining that weighted logistic regression addresses the problem of varying action values by assigning weights to each sample based on the value of the observed action. Then, walk through how you would define the weights (e.g., using business value or engagement depth), train the model, and evaluate its performance, emphasizing the trade-offs and practical considerations.
Pro tip: Mention that weights should be normalized to keep the effective sample size stable and to avoid numerical issues, and that you should validate the weighting scheme with offline metrics and online A/B tests to ensure it aligns with business goals.
Clarify the prediction goal (e.g., probability of a valuable engagement) and assign a numerical value to each action (open, creation, share, save) based on business impact or user engagement depth.
Use weighted logistic regression where each sample's contribution to the loss is scaled by the value of its observed action. This can be done by passing sample weights to the model during training.
Normalize weights to prevent numerical instability, consider class imbalance, and ensure the weighting scheme doesn't overly bias the model. Discuss potential alternatives like multi-task learning or ordinal regression.
Evaluate the model using weighted metrics (e.g., weighted log-loss, AUC) and validate with online experiments. Iterate on the action values based on business outcomes.
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Start by clarifying the ML use case and system requirements, then outline the core entities and their relationships in the feature store schema. Focus on how the schema supports both offline training and online serving, ensuring consistency and scalability.
Pro tip: Emphasize the importance of point-in-time correctness and how your schema design prevents data leakage, which is critical for production ML systems.
Ask questions to understand the ML use case, data sources, latency requirements, and scale. This ensures the schema design is tailored to the specific system.
Define the main entities such as user, content, and context, and their relationships. This forms the foundation of the feature store schema.
Propose tables for feature groups, features, and feature values, including metadata like timestamps and versioning. Consider both batch and streaming features.
Explain how the schema supports point-in-time correct joins for training and low-latency lookups for serving, possibly using a dual-store architecture.
Mention partitioning, indexing, and access control to handle Snapchat-scale data and ensure feature discoverability and reuse.
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Inverse propensity scoring was the answer they were looking for and I got there, but I initially started talking about randomization which is more of a data collection fix than a training fix.
Start by defining position bias and its impact on ranking models, then outline a systematic approach to detect and correct it. Emphasize the importance of unbiased evaluation and the trade-offs between different correction methods, especially in the context of Snapchat's large-scale recommendation systems.
Pro tip: Mention that position bias correction should be validated through online A/B tests, as offline metrics can be misleading. Also, highlight the need to balance bias correction with model performance and latency constraints.
Explain what position bias is and how it manifests in training data (e.g., items at higher positions get more clicks regardless of relevance). Describe methods to detect it, such as examining click-through rates by position or using randomization.
Discuss common techniques like inverse propensity scoring (IPS), position-based models, or adding position as a feature during training but removing it at inference. Highlight the trade-offs: IPS can be high variance, while position as a feature may not fully remove bias.
Detail how to implement the chosen method, such as estimating propensities from randomization or using a separate model to predict position bias. Emphasize the need for careful feature engineering and validation to avoid introducing new biases.
Stress the importance of evaluating the corrected model offline using unbiased metrics (e.g., from a holdout set with randomized positions) and then validating online via A/B tests. Mention that online metrics like engagement and satisfaction are ultimate judges.
Explain that bias correction is an ongoing process. Set up monitoring to detect drift in position bias and be prepared to retrain or adjust the correction method as user behavior or the system evolves.
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Talked about diversity constraints in post-processing, exploration budgets, and down-weighting popularity signals in the ranking model.
Start by clarifying what 'overexposing' means in Snapchat's context—likely the lens recommendation system disproportionately surfacing popular lenses, reducing diversity and discovery. Then propose a multi-pronged strategy: adjust ranking objectives to include diversity and fairness, implement exploration mechanisms, and monitor long-term ecosystem health. Emphasize trade-offs between short-term engagement and long-term user satisfaction and creator fairness.
Pro tip: Frame the solution around Snapchat's unique AR ecosystem: balance user engagement with creator opportunity and content freshness, and mention how you'd measure success beyond CTR, such as diversity metrics and creator retention.
Clarify what 'overexposing' means: is it about popularity bias in recommendations, lack of diversity, or creator fairness? Define success metrics like exposure diversity, creator coverage, and long-term user retention.
Identify why popular lenses dominate: feedback loops in ranking, lack of exploration, or objective misalignment. Consider data biases and system incentives.
Propose algorithmic changes: diversity-aware ranking, exploration-exploitation (e.g., epsilon-greedy, Thompson sampling), re-ranking with fairness constraints, or multi-objective optimization. Also consider product levers like curated collections or personalized discovery.
Discuss potential impacts on engagement, user satisfaction, and creator ecosystem. Suggest A/B testing and long-term monitoring to balance competing objectives.
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Covered feature store, model registry, versioned training specs, and lineage tracking.
Start by defining reproducibility and feature consistency as core requirements, then outline the key components of an ML platform that address them: feature store, versioning, lineage, and monitoring. Emphasize how these components work together to ensure identical feature computation offline and online, and discuss trade-offs like latency vs. consistency.
Pro tip: Highlight the importance of a feature store with a unified transformation API and point-in-time correctness to prevent training-serving skew. Mention that at scale, you'd also need to handle schema evolution and backfill strategies without breaking online serving.
Clarify what reproducibility and consistency mean: same code, data, and environment produce same results; features computed identically offline and online. Mention the need for auditability and debugging.
List essential platform components: feature store (offline/online), model registry, experiment tracking, data versioning, and orchestration. Explain how each supports reproducibility and consistency.
Detail the feature store's role: unified transformation logic (e.g., using a DSL or shared library), point-in-time correct joins for training, low-latency serving for online, and materialization pipelines.
Explain versioning of data, features, models, and code, plus lineage tracking to trace from model predictions back to raw data. This ensures reproducibility and facilitates debugging.
Discuss monitoring for feature drift, skew detection, and data quality. Include automated validation to catch inconsistencies between offline and online features.
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Asked about the surface being ranked, latency budget, and whether lenses were user-generated or curated.
Start by framing the problem as an ML design task, then list clarifying questions across key dimensions like product goals, data, constraints, and success metrics. For each area, state a reasonable assumption you'd make if answers aren't available, and explain how that assumption de-risks the project.
Pro tip: Prioritize questions that directly impact model architecture or data requirements, and explicitly tie each assumption to a fallback plan—this shows you can move forward without blocking on perfect information.
Ask what user problem the model solves, how it integrates into Snapchat, and what success looks like (e.g., engagement, retention). Assume a specific goal like increasing story views if unspecified.
Ask about data sources, volume, labeling, and privacy constraints. Assume you have access to historical user interaction logs with implicit feedback if not specified.
Ask about latency, compute budget, model size, and deployment environment (on-device vs. server). Assume server-side inference with <100ms latency for a feed ranking model.
Ask how success will be measured (offline and online) and what baseline exists. Assume you'll use AUC for offline and A/B test on CTR/engagement, with a simple heuristic as baseline.
Summarize your assumptions, note which are riskiest, and describe how you'd validate them early (e.g., small-scale experiment, data audit).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.