I started with a two-tower retrieval setup for candidate generation and moved into a ranking layer with a lightweight gradient boosted model for latency reasons.
Start by clarifying requirements and scale (e.g., DAU, catalog size, latency SLA), then walk through the classic two-stage architecture: candidate generation (retrieval) and ranking, followed by online serving details. Emphasize trade-offs at each stage, especially how low latency constraints shape model complexity and serving infrastructure.
Pro tip: Anchor your design around Reddit's unique content dynamics—user-generated posts, voting signals, and community (subreddit) context—and discuss how you'd handle cold-start and feedback loops, which are critical for a recommendation system at Reddit's scale.
Ask about scale (users, items, QPS), latency SLA (e.g., p99 < 200ms), and business goals (engagement, diversity). Define success metrics like CTR, watch time, or upvote rate.
Propose multiple retrieval sources: collaborative filtering (e.g., matrix factorization, two-tower models), content-based (embeddings of post text/images), and trending/popularity. Discuss how to combine them and handle cold-start.
Describe a multi-stage ranking system: a lightweight model (e.g., logistic regression or small NN) for coarse ranking, then a heavier model (e.g., deep neural network with user/item features) for fine ranking. Mention feature engineering and online learning.
Explain how to serve predictions with low latency: precompute embeddings, use ANN indexes (e.g., FAISS, ScaNN) for retrieval, cache popular results, and deploy models with optimized inference (e.g., TensorFlow Serving, ONNX). Discuss fallbacks and degradation strategies.
Cover offline metrics (recall@k, NDCG) and online A/B testing. Mention logging for feedback loops, bias mitigation, and how to monitor latency and model drift.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and scale of Reddit's recommendation system, then propose a feature store architecture that separates offline and online stores but ensures consistency through a unified transformation pipeline. Emphasize how you would handle online/offline consistency using techniques like point-in-time correctness, feature versioning, and a single source of truth for feature definitions.
Pro tip: Highlight the importance of monitoring feature drift and consistency metrics in production, and mention how you would design for backfilling and reprocessing to handle model retraining needs without disrupting online serving.
Ask about data volume, latency requirements, feature types (batch, streaming), and update frequency to tailor the design. Understand the specific needs of Reddit's recommendation system, such as real-time user interactions and content freshness.
Propose a scalable offline store (e.g., data lake with Parquet on S3, or a data warehouse like BigQuery/Snowflake) for storing historical features for training. Ensure it supports point-in-time correctness and efficient batch retrieval.
Suggest a low-latency online store (e.g., Redis, DynamoDB, Cassandra) for serving features in real-time. Discuss how to keep it updated with fresh features via streaming pipelines (e.g., Kafka, Flink) and batch imports.
Describe a unified feature engineering pipeline that generates features identically for both online and offline. Use feature versioning, point-in-time joins, and a feature registry to maintain consistency and avoid training-serving skew.
Cover monitoring for feature drift and consistency, backfilling strategies, and how to handle schema evolution. Discuss trade-offs between consistency, latency, and cost.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on the ordering guarantees piece.
Start by clarifying requirements and scale, then propose a high-level architecture that covers data collection, transport, storage, and processing. Emphasize how the pipeline supports ML use cases like training models for ranking and recommendations, and discuss trade-offs and monitoring.
Pro tip: Highlight the importance of data quality and idempotency to avoid double-counting events, and mention how you'd handle late-arriving data and schema evolution in a production system.
Ask about expected event volume, latency requirements, and what ML applications will consume the data. This ensures the design meets business needs and scales appropriately.
Propose client-side and server-side event capture with a standardized schema. Include mechanisms for batching, retries, and ensuring events are sent reliably.
Select a scalable message queue or streaming platform (e.g., Kafka, Kinesis) to handle high throughput and decouple producers from consumers. Discuss partitioning and replication for fault tolerance.
Outline a lambda architecture or similar with real-time stream processing (e.g., Flink, Spark Streaming) for immediate metrics and batch processing for historical analysis. Store raw events in a data lake and processed data in a warehouse or feature store.
Explain how the pipeline feeds ML models (e.g., feature engineering, training data generation) and include monitoring for data quality, pipeline health, and model performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the most interesting part of the whole interview.
Start by explaining the entity (e.g., user, session, or item) and time window for joining signals, then describe the join logic (e.g., left join impressions to clicks and dwell time) and how you handle missing or delayed data. Emphasize the pitfalls you proactively address, such as selection bias, time leakage, and label noise, and how you validate the resulting dataset.
Pro tip: Mention that you always simulate the production join logic in a staging environment and compare offline metrics to online A/B results to catch subtle leakage or bias early.
Clarify the unit of analysis (user, session, item) and the time window for each signal (e.g., impression within 30 days, click within 1 hour, dwell time after click). This ensures consistent aggregation and avoids mixing incompatible granularities.
Decide on the join type (e.g., left join impressions to clicks and dwell time to preserve all impressions) and the join keys (e.g., impression_id, user_id, item_id). Consider using window functions or as-of joins for time-based relationships.
Address nulls from missing clicks or dwell time by imputing (e.g., zero dwell for no click) or filtering, and account for delayed events (e.g., clicks arriving after the join) by using a cutoff or watermark.
Identify and correct for selection bias (e.g., only users who click have dwell time), time leakage (e.g., using future clicks to predict past impressions), and feedback loops (e.g., dwell time influenced by ranking).
Validate the joined dataset by checking distribution shifts, missing rates, and correlation with business metrics. Iterate on join logic based on offline evaluation and online A/B tests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Covered the usual stuff: online metrics like CTR and dwell time, distribution shift on input features, and prediction score drift.
Start by outlining a layered monitoring strategy that covers system health, data quality, and model performance, with both offline and online metrics. Then explain how to detect degradation using statistical tests, drift detection, and business KPIs, and finally describe how to diagnose root causes and trigger alerts.
Pro tip: Emphasize the importance of setting up a feedback loop with A/B tests and guardrail metrics to catch subtle degradations before they impact user experience. Also, mention that you would monitor the distribution of recommendations to detect popularity bias or filter bubbles.
Identify three layers: system health (latency, throughput, errors), data quality (feature drift, missing values), and model performance (prediction accuracy, ranking metrics).
Set baselines from offline evaluation and historical production data, and define alert thresholds using statistical process control or percentile-based limits.
Track online metrics like CTR, dwell time, and engagement, and offline metrics like NDCG, recall@k, and calibration, using delayed feedback where necessary.
Use techniques like PSI, KL divergence, and sequential hypothesis testing to detect data drift and performance drops, and monitor for concept drift.
When degradation is detected, drill down by segment, feature, and model version to find root cause, and set up automated alerts with clear escalation paths.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Standard enough question but they wanted specifics on how to handle interference between treatment and control when users interact with shared content.
Start by outlining the end-to-end experimentation lifecycle, from hypothesis to deployment, emphasizing safety and statistical rigor. Then detail the technical infrastructure for A/B testing recommendation models, including metrics, guardrails, and rollout strategies. Finally, discuss how to iterate based on results while mitigating risks like feedback loops and novelty effects.
Pro tip: Always define guardrail metrics (e.g., user retention, diversity) alongside primary metrics to catch unintended consequences early. Use sequential testing or Bayesian methods to allow early stopping without inflating false positives.
Clearly state the goal of the experiment (e.g., increase CTR or watch time) and formulate a testable hypothesis for the recommendation model change. Align with product and business metrics.
Choose primary, secondary, and guardrail metrics. Determine sample size, power, and randomization unit (e.g., user-level). Plan for A/A tests to validate the setup.
Implement a scalable experimentation platform that supports easy assignment, logging, and analysis. Ensure data pipelines capture model predictions and user interactions reliably.
Run the experiment with a small percentage of traffic initially, monitoring guardrails and system health. Use sequential testing or Bayesian methods for early stopping if needed.
Analyze results with statistical rigor, considering novelty and primacy effects. If successful, gradually roll out to more users; if not, learn and iterate. Document findings for future experiments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.