The 60-second answer
Define relevance, corpus scale, update rate, latency, safety/rights filters, and whether exact lexical signals should complement semantic retrieval. Train/use aligned text-image encoders, precompute image embeddings, build an ANN index, retrieve candidates, then optionally rerank with a richer cross-modal model.
Build the answer in this order
Define relevance, corpus scale, update rate, latency, safety/rights filters, and whether exact lexical signals should complement semantic retrieval.
Train/use aligned text-image encoders, precompute image embeddings, build an ANN index, retrieve candidates, then optionally rerank with a richer cross-modal model.
Use hard negatives and query/image diversity in training; handle new images and index refresh without full rebuilds where possible.
Evaluate recall@K/NDCG, semantic failure slices, tail queries, latency, index freshness, and online search success.
A useful interview mental model
This is the shape of a strong answer—not a script to memorize.
Senior-level signal
- Senior answers separate embedding-model quality from ANN approximation/index quality.
- Discuss multimodal safety/rights filtering, embedding version migration, and shadow indexes for safe rollout.
What the interviewer is really testing
Likely follow-up questions
Common weak-answer patterns
- Jumping to a model before defining the product contract.
- Listing components without bottlenecks, metrics, or failure handling.
- Ignoring data quality, serving latency, monitoring, and iteration.