This is a beast of a question and I underestimated how much they'd push on the 'why' behind every choice.
Start by clarifying the product context and business objectives, then walk through the ML system design in layers: data, features, models, and serving. Emphasize trade-offs, metrics, and how you would iterate based on feedback.
Pro tip: At Uber, tie your design to real-time constraints and marketplace dynamics—show you understand that recommendations must balance multiple stakeholders (riders, drivers, eaters, restaurants) and that latency and freshness are critical.
Ask questions to understand the product (e.g., Uber Eats feed, Uber rider home screen) and define success metrics like CTR, conversion, or retention. Align the design with business objectives such as increasing orders or reducing ETA.
Outline data sources (user interactions, item metadata, context) and how to build features (user, item, context) in batch and real-time. Discuss handling of implicit feedback and cold-start.
Propose a multi-stage architecture: candidate generation (e.g., two-tower, ALS) followed by ranking (e.g., GBDT, deep learning). Explain how to incorporate business rules and diversity.
Describe the serving stack: offline training, online inference with low latency, caching, and A/B testing. Discuss how to handle real-time updates and scale.
Define offline metrics (e.g., recall@k, NDCG) and online metrics (CTR, conversion). Explain how to run experiments, monitor, and iterate based on feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Came up as a follow-up but felt like its own question.
Start by outlining the data sources relevant to Uber's business, such as trip data, GPS logs, and user feedback, and then discuss labeling strategies like manual annotation, weak supervision, and active learning. Emphasize a systematic approach to bias detection and mitigation, including fairness metrics and debiasing techniques, and tie it back to business impact and ethical considerations.
Pro tip: Highlight the importance of continuous monitoring and feedback loops in production to catch biases that emerge over time, and mention how you would collaborate with cross-functional teams like legal and product to define fairness constraints.
List internal sources like trip records, driver and rider app events, and external sources like weather or traffic data, considering volume, velocity, and variety.
Discuss trade-offs between manual labeling, semi-supervised techniques, and leveraging implicit feedback (e.g., ratings) to balance cost, quality, and scalability.
Explain methods to identify biases, such as analyzing representation across geographies, demographics, and time, and using fairness metrics like demographic parity.
Describe techniques like reweighting, resampling, or adversarial debiasing, and how to validate their effectiveness without harming model performance.
Emphasize setting up monitoring dashboards and feedback loops to track bias metrics post-deployment and retrain models as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem context and requirements (e.g., scale, latency, relevance metrics). Then describe a two-stage pipeline: a fast candidate generation stage (e.g., using ANN or matrix factorization) followed by a more accurate ranking stage (e.g., GBDT or deep model). Finally, discuss trade-offs at each stage, such as recall vs. precision, latency vs. accuracy, and complexity vs. maintainability.
Pro tip: Emphasize that the two-stage design is a trade-off between efficiency and effectiveness, and that the choice of algorithms depends on business metrics (e.g., Uber's need for real-time ETAs and personalization). Mention how you would evaluate and iterate on each stage separately.
Ask about scale (users, items), latency requirements, and key metrics (e.g., CTR, conversion). This sets the stage for justified design choices.
Propose methods like ANN, matrix factorization, or heuristic rules to quickly retrieve a few hundred candidates from millions. Discuss trade-offs: recall vs. speed, and simplicity vs. personalization.
Describe using more complex models (e.g., GBDT, DNN) to score and rank the candidates. Trade-offs: accuracy vs. latency, feature richness vs. inference cost, and model complexity vs. interpretability.
Explain how the stages connect (e.g., candidate set size, feature consistency) and how to evaluate end-to-end (offline metrics, online A/B tests). Mention iterative improvements.
Conclude with a balanced view: e.g., for Uber, prioritize low-latency candidate generation and a ranking model that balances accuracy with real-time constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through Faiss vs ScaNN, pre-computed embeddings cached for users, latency budget split across retrieval and ranking.
Start by clarifying the scale (QPS, latency SLA, data volume) and the use case (e.g., real-time recommendations, search). Then propose a layered architecture: a fast online serving layer with caching and an approximate nearest neighbor (ANN) index, supported by offline batch pipelines for index building and updates. Discuss trade-offs between latency, accuracy, cost, and freshness, and justify your choices with Uber-specific examples like Michelangelo or Horovod.
Pro tip: Emphasize that at Uber's scale, you must design for failure and degradation—e.g., fallback to a simpler model or cached results if the ANN index is slow or unavailable. Also, mention that you'd measure p99 latency, not just average, and continuously monitor index recall and staleness.
Ask about expected QPS, latency SLA (e.g., p99 < 100ms), data size, update frequency, and accuracy needs. This scopes the problem and shows you avoid premature optimization.
Propose a multi-tier system: a load balancer, stateless model servers, a feature store for low-latency feature retrieval, and a caching layer (e.g., Redis) for frequent queries. Discuss horizontal scaling and autoscaling.
Compare ANN algorithms (HNSW, IVF, PQ) on recall, latency, memory, and build time. Explain how you'd shard the index across nodes and handle updates (e.g., periodic rebuilds vs. incremental).
Describe caching strategies: exact-match cache for repeated queries, semantic cache for similar embeddings, and precomputed results for hot items. Include fallback to a simpler model or stale cache if the index is slow.
Define metrics: latency percentiles, cache hit rate, index recall, and business KPIs. Set up A/B tests and canary deployments to validate changes without regressions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer: I said track prediction score distribution over time, monitor feature drift, and set up alerts on business metrics diverging from model scores.
Start by outlining a comprehensive monitoring system that tracks both model performance and business metrics in real-time, with automated alerts for anomalies. Then, describe a data-driven retraining strategy that balances model degradation, business impact, and operational costs, using statistical tests to decide when to retrain.
Pro tip: Emphasize the importance of setting up a feedback loop with product teams to capture ground truth labels and business context, as this is often overlooked but critical for timely retraining. Also, mention the trade-off between retraining frequency and computational cost, showing you understand the operational constraints.
Identify key model performance metrics (e.g., AUC, RMSE) and business metrics (e.g., conversion rate, revenue per user) to track continuously. Establish baselines and thresholds for alerts.
Set up dashboards and automated alerts using tools like Prometheus, Grafana, or internal systems. Ensure real-time data pipelines and logging for model inputs, outputs, and performance.
Use statistical process control, drift detection (e.g., PSI, KL divergence), and A/B tests to identify when model performance deviates significantly from baseline.
Define triggers such as performance drop below threshold, significant data drift, or scheduled intervals. Assess business impact and cost-benefit of retraining.
Retrain model with updated data, validate offline, and deploy via A/B test to confirm improvement before full rollout. Monitor post-retraining performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.