I started with the classic two-tower candidate retrieval into a ranker setup, which felt safe.
Start by clarifying requirements and constraints, then propose a high-level architecture that separates the home feed and 'up next' systems while sharing common components. Dive into data flow, model choices, and trade-offs, emphasizing how you'd evaluate and iterate.
Pro tip: Highlight the importance of cold-start and feedback loops, and discuss how you'd balance exploration and exploitation to avoid filter bubbles—showing awareness of Reddit's diverse content and community dynamics.
Ask about scale (users, videos), latency requirements, content types, and business goals (e.g., engagement, diversity). Confirm whether the system is for logged-in users, anonymous, or both.
Outline the main components: data ingestion, feature store, candidate generation, ranking, and serving. Explain how home feed and 'up next' differ in context and can share infrastructure.
Describe data sources (user interactions, video metadata, social signals) and features (user embeddings, video embeddings, contextual features). Mention real-time vs batch processing.
Propose a two-stage system: candidate generation (e.g., collaborative filtering, two-tower) and ranking (e.g., deep learning with multi-task objectives). Discuss how to handle 'up next' with session-based models.
Define offline metrics (recall@k, NDCG) and online metrics (CTR, watch time, diversity). Explain A/B testing, feedback loops, and how to monitor and retrain models.
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 purpose and the ML use cases (e.g., ranking, recommendations, moderation) to determine which events matter. Then propose a logging schema that captures raw user-item interactions with rich context and metadata, ensuring it supports feature engineering, labeling, and point-in-time correctness for training.
Pro tip: Emphasize designing logs with a clear separation between immutable raw events and derived features, and always include timestamps and versioning to enable reproducible training and avoid data leakage.
Ask about the product surface (e.g., feed, comments, ads) and the ML tasks (e.g., ranking, recommendation, abuse detection) to prioritize which events to log.
List user actions (impressions, clicks, upvotes, comments, shares, reports) and system events (model predictions, scores, experiment IDs) that provide signal for training.
For each event, specify fields: user_id, item_id, timestamp, event_type, context (device, session, page), and model-related fields (prediction, score, model_version).
Include labels (e.g., explicit feedback, downstream actions), handle missing values, and design for point-in-time joins to prevent leakage.
Use a flexible schema (e.g., Avro/Protobuf) with versioning, and consider storage formats (Parquet) and partitioning for efficient batch and streaming training.
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Knew position bias was coming so I had an answer ready: log position, use inverse propensity weighting or randomize position in a small slice of traffic.
Start by acknowledging that feed data is inherently biased by the ranking system and user behavior, then outline a systematic approach to identify and mitigate these biases. Focus on practical techniques like randomization, counterfactual logging, and debiasing methods, and tie them to A/B testing and trade-offs.
Pro tip: Emphasize that perfect debiasing is impossible, so you should design experiments to measure the impact of bias and iteratively improve. Mention that logging propensities (probabilities of exposure) is crucial for many debiasing techniques.
Discuss common biases in feed data: position bias, selection bias, exposure bias, and popularity bias. Explain how they arise from the ranking algorithm and user interactions.
Log the probability of each item being shown (propensity scores) and contextual features. This enables counterfactual reasoning and debiasing.
Use randomized traffic splits or epsilon-greedy exploration to collect unbiased data. Discuss trade-offs between exploration and user experience.
Use methods like inverse propensity scoring (IPS), counterfactual risk minimization, or unbiased learning-to-rank. Mention that these require careful validation.
Use A/B tests and offline evaluation to measure debiasing effectiveness. Continuously monitor for new biases as the system evolves.
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Start by clarifying the requirements and scale, then propose a unified feature store architecture that serves both online and offline features from a single source of truth. Emphasize techniques like point-in-time correctness, feature versioning, and monitoring to prevent training-serving skew.
Pro tip: Highlight the importance of a feature registry with metadata and lineage, and mention how you would handle backfilling and online-offline consistency checks in production. This shows you understand the operational challenges beyond just the initial design.
Ask about data volume, latency requirements, feature types (batch vs. streaming), and existing infrastructure. This ensures your design is tailored to Reddit's scale and needs.
Propose a centralized feature store that computes features once and serves them to both online (low-latency) and offline (batch) systems. Use a dual-store approach: a low-latency database (e.g., Redis) for online serving and a data warehouse (e.g., BigQuery, Snowflake) for offline training.
Implement time-travel joins to generate training datasets that reflect the feature values as they were at the time of prediction. This prevents data leakage and ensures offline models match online behavior.
Version features to allow safe updates and rollbacks. Monitor for training-serving skew by comparing online and offline feature distributions and alerting on discrepancies.
Discuss backfilling strategies, handling late-arriving data, and ensuring consistency during feature updates. Mention the need for automated consistency checks and a process for feature deprecation.
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Start by framing the ranking problem in terms of Reddit's core objectives: maximizing long-term user engagement and satisfaction. Then, describe a multi-objective approach where you define labels for different signals (e.g., clicks, upvotes, comments, watch time) and train separate models or a multi-task model, combining them with learned weights or a fusion layer. Emphasize the importance of balancing these signals through techniques like weighting, normalization, and online experimentation to avoid over-optimizing for short-term metrics.
Pro tip: Highlight the distinction between explicit and implicit feedback, and discuss how you'd handle position bias and feedback loops—this shows you understand real-world ranking challenges beyond textbook solutions.
Clarify what Reddit wants to optimize, such as daily active users, time spent, or content diversity. Map these to measurable proxy metrics like clicks, upvotes, comments, and watch time.
Decide on labels for each signal: binary for clicks/upvotes, continuous for watch time, and possibly ordinal for engagement depth. Consider a multi-task learning setup with shared representations and task-specific heads.
Discuss techniques to balance signals: weighting, normalization, or using a fusion model. Mention handling position bias via inverse propensity scoring and debiasing methods.
Propose offline evaluation with metrics like NDCG and online A/B tests measuring long-term user satisfaction. Use counterfactual evaluation to estimate impact of new models.
Explain how to detect and mitigate feedback loops (e.g., popularity bias) through exploration and diversity constraints, ensuring the model doesn't reinforce existing biases.
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Said I'd set a hard timeout on the ranker and fall back to a precomputed ranked list cached per user.
Start by framing latency budget as a product requirement tied to user experience, then describe a tiered fallback strategy that degrades gracefully from the full ranking model to simpler heuristics. Emphasize monitoring, dynamic timeouts, and precomputed results to stay within budget while maintaining relevance.
Pro tip: Mention that fallbacks should be pre-warmed and tested regularly, and that you should log when fallbacks trigger to detect model degradation early. Also, consider caching frequent queries to reduce model load.
Establish the maximum allowed latency for the ranking step based on overall page load targets and user expectations. Break down the budget across components (e.g., feature fetching, model inference, post-processing).
Create a hierarchy of fallbacks: (1) use a smaller, faster model; (2) use cached/precomputed rankings; (3) fall back to non-ML heuristics (e.g., popularity, recency); (4) return empty or default ranking. Ensure each tier has its own latency and quality trade-offs.
Set adaptive timeouts based on current system load and use circuit breakers to quickly switch to fallbacks when the model is slow or unavailable. Monitor error rates and latency percentiles to trigger fallbacks automatically.
Log fallback activations, measure their impact on engagement metrics, and regularly test fallback paths (e.g., chaos engineering). Use A/B tests to compare fallback quality against the primary model.
Continuously refine the latency budget and fallback logic based on production data. Explore model optimization (quantization, distillation) and caching to reduce reliance on fallbacks.
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Start by defining what drift means for your specific model and metrics, then describe a monitoring system that tracks both data distributions and model performance over time. Explain how you would set up alerts, investigate root causes, and take action—whether retraining, adjusting thresholds, or rolling back—while emphasizing the importance of feedback loops and continuous improvement.
Pro tip: Tie your answer to business impact: explain how you'd prioritize drift that affects key metrics like user engagement or revenue, and propose a cost-sensitive alerting strategy to avoid alert fatigue.
Clarify what constitutes drift for your model (e.g., feature distribution shifts, concept drift) and set baseline performance metrics and data distributions from training or a stable period.
Set up automated monitoring for input features, predictions, and outcomes (if available), using statistical tests (e.g., PSI, KL divergence) and performance metrics, with thresholds that trigger alerts.
When alerts fire, analyze which features or segments drifted, check for data pipeline issues, and determine if the drift is due to seasonality, upstream changes, or genuine concept shift.
Decide on mitigation: retrain with recent data, adjust model thresholds, roll back to a previous version, or implement a fallback heuristic, prioritizing based on impact and urgency.
Document the incident, update monitoring thresholds, and integrate learnings into the retraining pipeline to improve future drift detection and response.
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Start by acknowledging that logging is essential for ML system observability but must be balanced with privacy and retention constraints. Then, outline a structured approach that covers data minimization, anonymization, access controls, and retention policies, tailored to Reddit's user-generated content and ML needs.
Pro tip: Emphasize that privacy considerations should be integrated into the logging design from the beginning, not as an afterthought, and mention specific techniques like differential privacy or k-anonymity to show depth.
Determine what types of data will be logged and classify them based on sensitivity, such as PII, user content, or model parameters. Consider Reddit's context where logs may contain usernames, IP addresses, or post content.
Log only what is necessary for debugging and monitoring, and anonymize or pseudonymize sensitive fields. Techniques include hashing identifiers, tokenization, or aggregating data.
Set clear retention periods based on data utility and legal requirements, with automatic deletion after expiration. Consider tiered retention: short-term for raw logs, longer for aggregated metrics.
Restrict log access to authorized personnel and log all access to the logs themselves. Use role-based access control and encryption at rest and in transit.
Align with regulations like GDPR and CCPA, and document policies. Be transparent with users about logging practices in privacy policies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.