This was basically the whole interview crammed into one prompt.
Structure your answer around the ML lifecycle: define the prediction target (e.g., click or conversion probability), then describe label generation, feature engineering, model selection, and evaluation. Emphasize trade-offs between offline metrics and online A/B tests, and proactively discuss failure modes like position bias and cold start.
Pro tip: Frame the system as a multi-stage ranking pipeline (retrieval → ranking → re-ranking) and highlight how you'd handle counterfactual logging and delayed feedback, which are common in ads.
Choose targets like CTR, CVR, or GMV, and explain how to construct training labels from logged events, addressing delayed conversions and negative sampling.
List user, ad, and context features, then propose a model (e.g., deep neural network with embeddings) and justify why it suits the ranking task.
Describe data collection, feature store, training schedule, and low-latency serving with a two-stage retrieval and ranking system.
Cover offline metrics (AUC, NDCG), online A/B testing with guardrail metrics, and long-term holdout experiments.
Discuss at least three failure modes (e.g., position bias, feedback loops, cold start) and propose solutions like inverse propensity weighting or exploration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Came up as a follow-on after I glossed over it.
Start by defining position bias and its sources in your data, then outline a structured mitigation strategy that includes detection, correction, and validation. Emphasize the risks of ignoring it, such as skewed model performance and ethical issues, and tie your answer to Meta's scale and user impact.
Pro tip: Mention that position bias often interacts with other biases (e.g., selection bias) and that mitigation should be evaluated using online metrics like CTR and fairness audits, not just offline metrics.
Explain what position bias is (e.g., users click top results more regardless of relevance) and how to detect it using methods like randomization, swapping, or click models.
Describe techniques to correct for position bias, such as inverse propensity scoring (IPS), position-based click models, or adding position as a feature during training but removing it at inference.
Outline how to validate the effectiveness of bias mitigation using A/B tests, counterfactual evaluation, and fairness metrics, and set up ongoing monitoring.
Discuss the consequences: biased models that reinforce existing biases, poor generalization, reduced user trust, and potential ethical and legal issues.
Tie your answer to Meta's context: large-scale data, diverse user base, and the need for robust, fair AI systems that align with Meta's responsible AI principles.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Cold start is such a classic problem and I still managed to give a mediocre answer.
Start by clarifying the ranking objective and available data, then propose a mix of shop-level, user-level, and interaction features. For cold start, describe a fallback strategy using content-based features and exploration, and explain how you would transition to personalized models as data accumulates.
Pro tip: Emphasize that cold start is not just a modeling problem but also a product and data collection opportunity—suggest logging impressions and using bandit algorithms to efficiently gather feedback.
Ask about the ranking goal (e.g., relevance, conversion), the target metric, and what data is available for shops and users. This ensures your feature suggestions are aligned with business objectives.
List features such as shop historical performance (CTR, conversion rate), user-shop interaction history, contextual features (time, location), and shop attributes (category, price range).
For new shops, rely on intrinsic shop attributes (e.g., category, description embeddings, images) and user preferences to compute similarity-based scores. Also consider using user demographics and context.
Use multi-armed bandits or epsilon-greedy to explore new shops and gather data. Fallback to popularity or editorial rankings if no personalized signal is available.
Describe how to gradually incorporate learned features as data accumulates, and how to evaluate cold-start performance (e.g., A/B tests, offline metrics like coverage and diversity).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's goal and success metrics, then outline a two-phase evaluation: offline using historical data and online via A/B testing. For the A/B test, describe key guardrails like sample size, duration, and monitoring for novelty effects and metric regressions.
Pro tip: Emphasize that offline metrics are proxies and may not perfectly correlate with online impact; always validate with a small-scale online test before full rollout. Also, mention the importance of pre-registering the analysis plan to avoid p-hacking.
Clearly state the system's purpose and identify primary success metrics (e.g., CTR, conversion) and guardrail metrics (e.g., latency, user satisfaction).
Use historical data to simulate the system's performance. Employ techniques like cross-validation, holdout sets, and counterfactual evaluation to estimate impact.
Design an A/B test with proper randomization, control, and treatment groups. Determine sample size and duration based on power analysis.
Set up automated alerts for metric regressions, ensure no interference between groups, and monitor for novelty and primacy effects. Use sequential testing if needed.
After the test, analyze results with statistical rigor, check for heterogeneous treatment effects, and decide on rollout or iteration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.