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Similarity search engineers are rare. The skill combines machine learning (embeddings), systems engineering (index building), and performance optimization (latency vs recall trade-offs). Most ML engineers don't know FAISS. Most systems engineers don't know HNSW from IVF. Here's what to look for.
Must-Know Concepts
A qualified candidate understands:
- ✦ Index types: Flat (brute force), IVF (inverted file), HNSW (graph-based), PQ (product quantization)
- ✦ Speed vs accuracy trade-offs (recall@k vs QPS vs memory)
- ✦ Training vs adding vectors (IVF requires training on sample)
- ✦ Quantization: how PQ reduces memory by 90%+
- ✦ GPU vs CPU: when each makes sense
Practical Assessment Task
Ask candidates to:
- ✦ Build an index on a sample dataset
- ✦ Compare at least two index types
- ✦ Measure recall and latency
- ✦ Document optimization decisions
- ✦ Present trade-off analysis
Interview Questions That Work
Look for: HNSW or IVF with quantization. Memory estimation (~600GB for HNSW, ~60GB for IVFPQ). GPU for training, CPU for serving trade-offs.
IVF: partitions vectors into cells. HNSW: builds graph connections. IVF better for exact recall with quantization. HNSW better for high recall but higher memory.
Recall@k (fraction of ground truth nearest neighbors found within top-k), precision, QPS, and latency. Also A/B testing on business metrics.
Red Flags
Walk away if they:
- ✦ Can't explain different index types
- ✦ Has never measured recall@k
- ✦ Only used FAISS in Jupyter notebooks, never in production
- ✦ Doesn't understand memory constraints
- ✦ Claims one index is always best (no trade-off awareness)
What to Look For in a Portfolio
Signs of real experience:
- ✦ Production system with >1M vectors
- ✦ Benchmark results comparing index types
- ✦ Discussion of trade-offs made (accuracy vs speed vs memory)
- ✦ Open source contributions to FAISS or similar libraries
Team Fit Indicators
- ✦ Communicates trade-offs clearly
- ✦ Documents benchmarking methodology
- ✦ Understands production monitoring
- ✦ Collaborates with ML and platform teams
- ✦ Can estimate infrastructure costs
Hire the Right Expert
Similarity search is subtle. Small index choices dramatically impact latency and recall. Offline Pixel pre-vets similarity search engineers before you interview. Raise a request, talk to candidates, fund the project, and approve payment when the work is done.
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