This is where I spent most of the interview.
Start by defining the feature precisely: a time-decayed count of likes from the user to the author or post within the last 6 months. Then explain how to compute it offline with point-in-time correctness to avoid leakage, and how to serve it online with low latency using a feature store or precomputed aggregates. Finally, discuss trade-offs like storage, staleness, and cold-start handling.
Pro tip: Emphasize point-in-time correctness: when generating training data, only use interactions that occurred before the label timestamp. This is the most common source of leakage in temporal features and interviewers love to see you catch it.
Specify the exact feature: e.g., number of likes from user U to author A in the last 6 months, possibly with time decay or separate counts for post-level. Clarify how it will be used in the model (as a dense feature, bucketized, etc.).
Describe how to compute the feature from historical interaction logs. Use a time-windowed aggregation (e.g., sliding window over 6 months) and ensure that for each training example, only interactions before the label timestamp are included. Mention techniques like time-travel joins or snapshotting.
Explain how to serve the feature at inference time with low latency. Options: precompute aggregates in a key-value store (e.g., user-author like counts) and update incrementally, or use a feature store that supports real-time updates. Discuss trade-offs between freshness and cost.
Detail strategies to prevent leakage: strict temporal splits, excluding the current interaction from the count, and using only past data. Also mention the importance of consistent logic between offline and online computation to avoid training-serving skew.
Discuss cold-start (new users/authors), sparsity, and how to handle missing values. Suggest evaluating the feature's impact via offline metrics and online A/B testing, and monitoring for drift.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went straight to interaction features because I thought that was the sophisticated answer.
Start by clarifying the feature's nature (e.g., categorical, continuous, text) and the model architecture. Then discuss trade-offs between raw value, embedding, and interaction terms, emphasizing the need to balance model complexity, interpretability, and performance. Finally, propose an empirical evaluation plan to test each option.
Pro tip: Always tie your recommendation to the specific product context and business metrics—at Meta, demonstrating that you consider both offline model metrics and online user impact sets you apart.
Ask clarifying questions about the feature's type, cardinality, and relationship to existing features, as well as the current model architecture and constraints.
Compare raw value, embedding, and interaction term approaches based on their ability to capture signal, computational cost, and risk of overfitting.
Assess how each option impacts latency, interpretability, and maintainability, and align with product goals and metrics.
Suggest offline experiments (e.g., A/B tests on a holdout set) to compare options, and define success metrics (e.g., AUC, CTR, revenue).
Based on trade-offs and initial results, recommend a starting point and outline a plan to iterate, possibly combining approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through AUC and calibration, then slice analysis by content type and user activity level.
Start by clarifying the feature's goal and the primary success metric, then outline a validation plan that combines offline metrics with online A/B testing. Emphasize that offline metrics are proxies and should be validated online, and describe how you would slice the analysis to uncover heterogeneous effects.
Pro tip: Always tie offline metrics to the online business metric they aim to predict, and propose a guardrail metric to catch unintended consequences. Mention that you would pre-register the analysis plan to avoid p-hacking.
Ask what problem the feature solves and what the primary online metric is (e.g., CTR, engagement, revenue). Define what 'helps' means in measurable terms.
Choose metrics like AUC, precision@k, recall, NDCG, or calibration that are relevant to the feature. Explain why they are proxies for the online metric.
Use time-based splits to mimic production, avoid leakage, and evaluate on a holdout set. Consider counterfactual or off-policy evaluation if applicable.
Propose an A/B test with sufficient power, define primary and guardrail metrics, and specify duration and randomization unit.
Segment by user demographics, behavior, device, geography, and other relevant dimensions. Look for consistent improvements and potential harms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the hardest part and also the most interesting.
Acknowledge that offline-online gaps are common and often stem from differences in data, metrics, or user behavior. Structure your answer by first categorizing potential causes (data, metrics, model, experiment) and then outlining a systematic debugging process that involves validation, deeper analysis, and iteration.
Pro tip: Emphasize the importance of aligning offline evaluation with the online objective and using techniques like counterfactual logging or interleaving to bridge the gap. Show that you prioritize understanding the 'why' behind the metrics rather than just chasing numbers.
Check that the A/B test is properly set up: randomization, sample size, duration, and that the online metric is correctly defined and sensitive to changes. Ensure there are no bugs in logging or metric computation.
Investigate whether the offline training data matches the online serving data in terms of feature distributions, user segments, and temporal patterns. Look for covariate shift or concept drift.
Examine if the offline metric is a good proxy for the online metric. Consider if the online metric is too coarse or if the treatment effect is diluted across segments. Check for novelty effects or metric saturation.
Verify that the model is deployed correctly and performing as expected online. Check for training-serving skew, feature pipeline issues, or model staleness. Analyze if the model's predictions are actually influencing the user experience.
Break down the online results by user segments, time, and other dimensions to see if there are heterogeneous treatment effects. Perform power analysis to ensure the test had enough sensitivity to detect the expected lift.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.