Optimize vector semantic search for an assistant
Company: Microsoft
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Onsite
## Scenario
You own the vector semantic search layer for an AI assistant (e.g., Copilot). Users query across enterprise documents and/or product knowledge. Current issues:
- Low recall for relevant passages
- High latency and high cost
- Stale results when documents update
## Task
Design a plan to optimize the **end-to-end vector search** system.
### Requirements
- Support hybrid retrieval (keyword + semantic).
- Multi-tenant isolation (enterprise customers).
- Freshness within minutes of document updates.
- Clear evaluation methodology.
### What to cover
- Embedding model choices and training
- Indexing (ANN), sharding, filtering
- Hybrid search + reranking
- Caching and latency optimizations
- Metrics and offline/online experiments
Quick Answer: This question evaluates a candidate's competency in designing production-grade vector semantic search systems, including embedding model selection and training, ANN indexing and sharding, hybrid keyword-plus-semantic retrieval and reranking, caching and latency optimization, multi-tenant isolation, and freshness-aware update pipelines.