This is one of those questions that feels manageable until you realize how many directions it can go.
Start by clarifying the product context and requirements, then outline a high-level system architecture covering data sources, candidate generation, ranking, and serving. Dive into key ML components like feature engineering, model choices, and evaluation metrics, while discussing trade-offs and scalability.
Pro tip: Emphasize the importance of user context and real-time signals, and discuss how you would handle cold-start and diversity to avoid over-recommending popular places.
Ask questions to understand the product goals, user base, scale, and constraints (e.g., latency, privacy). Define success metrics like CTR, dwell time, or user satisfaction.
Identify data sources: user interactions (check-ins, searches, reviews), place attributes (category, location, popularity), and contextual signals (time, weather, user location). Discuss feature engineering for user, place, and context.
Propose a two-stage system: candidate generation (e.g., collaborative filtering, content-based, or geo-based retrieval) followed by ranking (e.g., gradient boosted trees or deep neural networks). Mention handling of cold-start and exploration.
Describe offline training with historical data, using metrics like recall@k for retrieval and NDCG for ranking. Plan online evaluation via A/B tests measuring business metrics.
Outline a scalable serving infrastructure with low-latency requirements, possibly using precomputed embeddings and approximate nearest neighbor search. Discuss monitoring and feedback loops.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Tricky because there's no single obvious label.
Start by clarifying the recommendation scenario (e.g., feed ranking, friend suggestions) and the business objective. Then define the prediction target as the specific user action you want to predict (e.g., click, like, share) and the training labels as the observed outcomes for that action, considering time windows and negative sampling. Emphasize how these choices impact model performance and align with Meta's goals.
Pro tip: Discuss how you would handle delayed feedback and position bias, and mention that you'd validate the label definition through offline metrics and online A/B tests to ensure it drives the desired business outcome.
Ask or state the specific recommendation surface (e.g., News Feed, Groups, Marketplace) and the primary business objective (e.g., engagement, revenue, user satisfaction).
Specify the exact user action to predict (e.g., click, like, comment, share, dwell time) and whether it's binary, multi-class, or regression. Consider multiple targets and how to combine them.
Describe how to derive labels from logged data: positive labels from observed actions, negative labels from non-actions, and how to handle missing data, time windows, and sampling.
Discuss issues like class imbalance, delayed feedback, position bias, and how label choice affects model bias and evaluation metrics.
Explain how you would validate the label definition offline (e.g., through holdout sets) and online (A/B tests), and iterate based on performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went through user history, location, time of day, search queries, map interactions, place metadata.
Start by clarifying the system's objective and constraints, then propose a mix of batch and real-time data sources, and finally detail feature engineering techniques including transformations, aggregations, and embeddings. Emphasize how features are computed, stored, and served to ensure consistency and low latency.
Pro tip: Highlight the importance of feature versioning and monitoring to prevent training-serving skew, and mention how you would leverage Meta's existing feature store infrastructure to accelerate development and ensure consistency.
Ask questions to understand the problem domain, prediction task, latency requirements, and scale. This will guide data source and feature choices.
List relevant internal and external data sources, such as user interactions, content metadata, social graph, and contextual signals. Consider batch and streaming sources.
Describe transformations for each data type: numerical (scaling, binning), categorical (one-hot, hashing, embeddings), text (TF-IDF, embeddings), and graph (node embeddings). Include temporal aggregations and windowed statistics.
Explain how features are stored (e.g., feature store) and served online (low-latency) and offline (for training). Discuss consistency between training and serving.
Outline monitoring for feature drift, data quality, and model performance. Describe how to iterate on features based on feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
New users I had covered: onboarding signals, demographic priors, popular local picks.
Start by clarifying the two cold start scenarios (new users and new places) and their distinct challenges, then propose a unified framework that leverages side information and meta-learning. Emphasize practical trade-offs at Meta's scale, such as using content-based features for new places and few-shot learning for new users, and discuss how to evaluate and iterate.
Pro tip: Show awareness of Meta's production constraints by mentioning how you'd handle cold start in a multi-task, multi-modal setting and how you'd measure success with online metrics like CTR and engagement, not just offline AUC.
Ask clarifying questions to understand what 'new' means (e.g., zero interactions vs. limited), what data is available (user demographics, place attributes), and the business impact (e.g., user retention, place discovery).
For new places, use content-based features (location, category, reviews) to compute embeddings; for new users, use demographic and contextual signals to bootstrap preferences.
Use meta-learning (e.g., MAML) to quickly adapt to new users/places from few interactions, and transfer knowledge from similar users/places via embeddings or graph neural networks.
Implement bandit algorithms (e.g., Thompson sampling) to balance showing new places to users and collecting feedback, while personalizing for new users via contextual bandits.
Define offline proxies (e.g., recall@k for new places) and online A/B tests measuring CTR, engagement, and retention; monitor for bias and feedback loops.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This came near the end and I was running low on steam.
Structure your answer around a systematic risk mitigation framework: first identify and measure each risk, then apply targeted technical and product interventions, and finally validate through rigorous experimentation with fairness-aware metrics. Emphasize that these risks are interconnected and require continuous monitoring and cross-functional collaboration.
Pro tip: Show maturity by acknowledging trade-offs—e.g., aggressive debiasing can hurt relevance—and propose a balanced approach with guardrail metrics in A/B tests. Mention that fairness definitions vary by region and require local context, demonstrating global product awareness.
Define metrics for popularity bias (e.g., Gini coefficient of item exposure), feedback loops (e.g., diversity over time), spam (e.g., precision/recall of spam classifiers), and fairness (e.g., demographic parity across regions). Establish baselines and monitoring dashboards.
For popularity bias, use re-ranking or inverse propensity weighting; for feedback loops, inject exploration or use causal debiasing; for spam, deploy adversarial training and user reporting; for fairness, use constrained optimization or post-processing to meet region-specific fairness criteria.
Work with product teams to design features that promote diverse content (e.g., 'explore' tabs), implement rate limiting and verification to combat spam, and adapt policies to local norms and regulations (e.g., different fairness definitions across regions).
Run A/B tests with guardrail metrics (e.g., fairness, spam rate, diversity) alongside engagement metrics. Use long-term holdouts to detect feedback loops and monitor regional disparities. Iterate based on results.
Deploy real-time monitoring for drift and anomalies, set up alerts for fairness violations, and establish a feedback loop with cross-functional teams to update models and policies as new risks emerge.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about pre-computing candidate sets and doing lightweight real-time re-ranking with context features.
Start by clarifying the system's requirements and scale, then propose a two-stage serving architecture with a fast retrieval layer and a more complex ranking layer. Emphasize how real-time features like location and time are fetched and incorporated with low latency, and discuss trade-offs between latency and model complexity.
Pro tip: Quantify latency budgets for each stage (e.g., retrieval <50ms, ranking <100ms) and mention techniques like caching, approximate nearest neighbor search, and feature pre-computation to meet those budgets.
Ask about the system's purpose, expected QPS, user base, and latency SLOs. Understand what real-time context is available and how critical it is for predictions.
Propose a retrieval stage to quickly narrow down candidates (e.g., using embeddings and ANN) and a ranking stage to score them with a more complex model. Explain how real-time features are integrated in each stage.
Describe how to fetch and process location and time-of-day features with low latency, using in-memory stores, precomputed aggregates, or streaming pipelines. Discuss fallbacks for missing data.
Set latency budgets for each component (e.g., feature fetch <10ms, retrieval <50ms, ranking <100ms) and propose optimizations like caching, model quantization, and parallel processing.
Acknowledge trade-offs between latency, accuracy, and cost. Mention the need for monitoring and A/B testing to ensure the system meets business goals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
A/B testing with holdout groups, measuring both engagement and diversity metrics, watching for cannibalization between recommendation surfaces.
Start by outlining a structured experimentation framework that covers offline evaluation, online A/B testing, and continuous feedback integration. Emphasize the importance of defining clear metrics, ensuring statistical rigor, and iterating quickly based on results. Highlight how you would handle challenges specific to live recommendation systems, such as feedback loops and cold start.
Pro tip: Demonstrate awareness of the trade-off between exploration and exploitation, and mention techniques like interleaving or multi-armed bandits to efficiently test multiple models. Also, stress the importance of guardrail metrics to catch regressions in user experience.
Clearly articulate the goal of the experiment (e.g., increase click-through rate or user engagement) and select primary and secondary metrics. Include guardrail metrics to monitor for negative side effects.
Use historical data to evaluate candidate models offline, employing techniques like counterfactual evaluation or replay to estimate performance before live testing. This helps filter out poor models and reduces risk.
Design and run controlled online experiments, randomizing users into control and treatment groups. Ensure proper sample size, duration, and statistical power to detect meaningful differences.
Continuously monitor experiment metrics for statistical significance and practical impact. Analyze segment-level performance and investigate any unexpected effects or interactions.
Based on results, decide whether to launch, iterate, or abandon the change. Incorporate learnings into the next cycle, and consider using bandit algorithms for continuous optimization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.