Design an ML-powered search system
Quick Overview
This question evaluates a candidate's ability to design end-to-end ML-powered search systems, testing competencies in information retrieval, ranking, semantic embeddings, indexing and freshness, personalization, scalability, latency constraints, and observability.
Atlassian
Mar 1, 2026, 12:00 AM
Machine Learning Engineer
Onsite
ML System Design
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Scenario
Design an end-to-end search system for a consumer product (e.g., an e-commerce marketplace or content platform) where users type queries and expect relevant, personalized results.
Requirements
Functional
- Given a query string, return a ranked list of results (items/documents/videos/etc.).
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Support:
- Keyword matching (lexical search).
- Semantic matching (synonyms/paraphrases via embeddings).
- Filters/sorts (category, price range, recency, etc.).
- Autocomplete/suggestions (optional but preferred).
- Handle frequent content updates (new/edited documents should become searchable quickly).
Non-functional (assume reasonable targets)
- Latency: p95 < 200 ms for the online request path.
- Scale: 10k QPS peak; corpus size ~100M documents.
- Reliability: graceful degradation if ML components fail.
- Observability: logging for debugging, offline training, and online evaluation.
What to cover
- High-level architecture (offline pipelines + online serving).
- Retrieval (candidate generation) and ranking.
- Indexing strategy and freshness.
- Model training data, labels, evaluation metrics.
- Personalization and cold-start.
- A/B testing and launch plan.
Solution
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