01
LeetCode covers coding. Not ML.
Grinding LeetCode won't help you design a recommendation system or explain why you'd choose a two-tower model over matrix factorisation.
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Feedback result
Interviewer rubric
Real-time recommendations
Strong retrieval and ranking instincts. Needs more depth on cold start, position bias, and offline vs online evaluation.
Candidate Generation5/5
Ranking4/5
Cold Start1/5
Specific improvement
Separate offline metrics like NDCG and recall@k from online metrics like watch time, retention, and experiment guardrails.
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02
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03
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Design a real-time recommendation system for a video streaming platform with 50 million daily active users. Walk through your approach from candidate generation to ranking.
Interviewer feedback preview
Staff-level recommendation system answer
Overall score: 17 / 25
Strong grasp of the retrieval and ranking pipeline with good architectural instincts. However the answer does not address cold start for new users or items, and the evaluation section conflates offline and online metrics without distinguishing between them. A senior candidate answer — would need more depth to pass at staff level.
Strengths
- Correctly proposed a two-tower model for candidate generation and justified the choice
- Identified approximate nearest neighbour search for scale — shows production awareness
- Mentioned watch time as the optimisation objective rather than just CTR
Areas to improve
- Cold start problem not addressed — this is a common follow-up question at every major tech company
- Evaluation section needs to clearly separate offline metrics (NDCG, recall@k) from online metrics (CTR, watch time, retention)
- No mention of how you'd handle position bias in implicit feedback data
Criteria breakdown
Problem Framing
3 / 5Objective defined but no clarifying questions asked
Candidate Generation
5 / 5Two-tower with ANN, well justified
Ranking
4 / 5Good feature coverage, missing position bias
Cold Start
1 / 5Only lightly acknowledged; no concrete new-user or new-item plan
Evaluation
4 / 5Good metric coverage, but offline and online trade-offs need sharper separation
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What you'll prepare for
ML System Design
End-to-end ML system design questions covering architecture, trade-offs, and production considerations
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Recommendation Systems
Prepare for recommendation system design, candidate generation, ranking, retrieval, and evaluation questions.
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Natural Language Processing
Study NLP model design, evaluation, retrieval-augmented generation, embeddings, and production tradeoffs.
5 questions
Computer Vision
Practice computer vision system design, data strategy, model evaluation, and deployment considerations.
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Ranking
Prepare for ranking systems, learning-to-rank, metrics, experimentation, and relevance tradeoffs.
5 questions
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