The four-stage pipeline (retrieval, pre-rank, rank, re-rank) is basically assumed knowledge going in.
Start by clarifying the problem scope and requirements, then outline the high-level system architecture covering data collection, candidate generation, ranking, and serving. Dive into the ML pipeline, discussing feature engineering, model choices, and evaluation metrics, while addressing trade-offs like latency vs. accuracy and scalability.
Pro tip: Emphasize the importance of real-time signals and user feedback loops, as Meta's systems heavily rely on fresh data to personalize content. Also, proactively discuss potential biases and mitigation strategies to show awareness of responsible AI.
Ask questions to understand scale, latency constraints, business goals, and available data. Define success metrics like engagement, retention, and diversity.
Sketch the end-to-end system: data ingestion, feature store, candidate generation, ranking, and serving. Mention offline vs. online components.
Detail candidate generation (e.g., two-tower models), ranking (e.g., deep learning with multi-task objectives), and feature engineering (user, video, context).
Explain offline metrics (AUC, recall@k) and online A/B testing. Discuss how to handle cold start and feedback loops.
Address trade-offs: latency vs. model complexity, exploration vs. exploitation, and scalability challenges (e.g., distributed training, serving at scale).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the product goal and user value, then define a north-star metric that captures long-term satisfaction. Work backward to structure a hierarchy of metrics: top-level health metrics, diagnostic metrics, and model-level objectives. Emphasize how offline model metrics connect to online A/B test outcomes and business impact.
Pro tip: Show that you understand trade-offs: e.g., optimizing for short-term engagement can hurt long-term retention, so include guardrail metrics. Also, mention that model objectives should be aligned with the north-star but may need proxies due to delayed feedback.
Understand what the recommendation system is trying to achieve for users and the business. This sets the foundation for all metrics.
Choose a single metric that best captures long-term user satisfaction and business success, such as daily active users or long-term retention.
Break down the north-star into sub-metrics (e.g., engagement, quality, diversity) and further into diagnostic metrics that can be directly influenced by the model.
Translate diagnostic metrics into model training objectives (e.g., CTR, watch time) and define guardrail metrics to prevent negative side effects.
Explain how offline model metrics predict online A/B test results, and how you would validate and iterate on the metric structure.
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My session apparently leaned hard into this.
Start by clarifying the goal and defining a clear hypothesis for the ranking model change. Then outline a structured A/B test plan covering randomization, metrics, sample size, and analysis, while addressing potential pitfalls like network effects and novelty effects.
Pro tip: Emphasize guardrail metrics and long-term holdout to ensure the change doesn't harm user experience or long-term engagement, and discuss how you'd handle interference in social networks.
Clearly state the change and the expected impact, then select primary, secondary, and guardrail metrics (e.g., CTR, engagement, user satisfaction).
Choose randomization unit (user, session), determine sample size and duration via power analysis, and set up control and treatment groups.
Mitigate novelty effects, primacy effects, and network interference by using techniques like long-term holdouts, cluster randomization, or switchback tests.
Launch the experiment, monitor for technical issues and guardrail violations, and ensure data quality.
Perform statistical analysis (e.g., t-test, bootstrapping), check for significance and practical impact, and decide whether to launch, iterate, or abandon.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the product context (e.g., feed ranking, friend recommendations, ads) and the scale of the social graph. Then outline a feature engineering strategy that combines node-level, edge-level, and graph-level features, and discuss how to handle the unique challenges of social graphs like sparsity, dynamism, and privacy.
Pro tip: Emphasize that social graph signals are most powerful when combined with user-item interaction features, and always mention privacy and fairness considerations—Meta cares deeply about responsible AI.
Ask questions to understand the specific task (e.g., ranking, recommendation), the scale (billions of nodes/edges), latency requirements, and privacy constraints. This shows you can scope the problem before diving into solutions.
List potential signals such as degree, centrality, community membership, tie strength, common neighbors, and interaction frequency. Categorize them into node-level, edge-level, and neighborhood-level features.
Explain how to compute these features efficiently (e.g., using graph embeddings like node2vec, GraphSAGE, or precomputed aggregates) and how to handle dynamic updates. Discuss trade-offs between batch and real-time computation.
Discuss sparsity (e.g., cold-start users), scalability (sampling, distributed processing), and privacy (differential privacy, federated learning). Mention how to evaluate feature importance and avoid leakage.
Describe how these features feed into the model (e.g., as embeddings, raw features), how to handle missing values, and how to monitor and update them in production.
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Inverse propensity weighting came up and I explained it reasonably well.
Start by defining position bias and training-serving skew, then outline a systematic approach to detect and mitigate position bias in training data, followed by a robust monitoring strategy for training-serving skew. Emphasize practical techniques like counterfactual logging, propensity scoring, and statistical tests, and tie them to real-world ML engineering practices.
Pro tip: Highlight the importance of logging serving-time features and predictions to enable continuous monitoring, and mention that at Meta, even small skews can significantly impact user experience due to scale. Also, discuss trade-offs between bias mitigation and model performance.
Explain what position bias is (e.g., users click top results more often regardless of relevance) and how to detect it using click models, propensity scores, or randomization.
Describe techniques such as inverse propensity scoring (IPS), counterfactual logging, or using unbiased estimators to reweight training examples.
Define skew as differences between training and serving data distributions or feature computations, and list common causes (e.g., feature pipeline inconsistencies, time-based drift).
Outline a monitoring plan: log serving features and predictions, compare distributions with training data using statistical tests (e.g., KL divergence, PSI), and set up alerts for significant deviations.
Emphasize continuous validation through A/B tests, shadow deployments, and feedback loops to ensure mitigation strategies remain effective and skew is addressed.
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Short answer: content-based fallback and exploration bandits.
Start by clarifying the problem scope and success metrics, then propose a two-sided solution: one for new users (user cold start) and one for new videos (item cold start). For each, outline a progression from content-based/feature-based methods to exploration and rapid online learning, and discuss how to evaluate and iterate.
Pro tip: Emphasize that cold start is not just a modeling problem but a product and systems problem—tie your solution to business metrics like user engagement and retention, and mention how you'd balance exploration costs with long-term gains.
Ask about the product context (e.g., feed, stories, reels), what signals are available at cold start, and define success metrics (CTR, watch time, retention).
Propose using onboarding signals, demographic/context features, and content-based recommendations; then transition to collaborative filtering as user data accumulates.
Leverage content features (visual, audio, text) and creator metadata to match videos to users; use multi-armed bandits or Thompson sampling for exploration.
Design a system that jointly handles both cold start types, using contextual bandits with shared embeddings and fast online updates to quickly learn from early interactions.
Define offline and online evaluation (A/B tests), monitor for biases, and plan for continuous improvement as more data arrives.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.