Fuse lexical and vector rankings
Design a hybrid retrieval system that combines lexical (BM25 inverted index) and dense vector (ANN) search and returns a single fused ranked list. The two retrievers disagree often: lexical nails exact-term/code/SKU queries, vectors nail paraphrase/semantic queries, and their scores are on totally different scales (BM25 unbounded, cosine in [-1,1]). Corpus: 200M documents, 8k QPS, p99 < 200ms. How do you run both, and — the crux — how do you fuse two incomparable score distributions into one ranking?
What a strong answer looks like
Clarify scale and constraints first. Propose a clean component breakdown, then go deep on the hard parts (data model, bottlenecks, consistency, failure modes) and name the trade-offs you are making.
Clarify5:00 left
Estimate5:00 planned
Design15:00 planned
Deep dive12:00 planned
Failure8:00 planned
Which questions mattered is sealed until you submit. Telling you now would just be handing over the edge cases.
Run or narrate your approach, then ask the coach.