The Hardest Embeddings & Vector Database Interview Questions (Staff Level)

This is the tier that separates senior from staff. Less definition, more judgment under real constraints: memory, latency, freshness, and cost. We cover the quantization Pareto frontier with byte math, Matryoshka embeddings, billion-scale sharding, embedding drift and zero-downtime re-indexing, DiskANN, the multimodal modality gap, embedding collapse and whitening, vector DB internals (tombstones and compaction), streaming vs batch builds, and end-to-end capacity planning. Chapters: Intro & roadmap Q1 - Quantization trade-offs (scalar/PQ/binary) Q2 - Matryoshka embeddings Q3 - Scaling to a billion vectors Q4 - Embedding drift & re-indexing Q5 - Disk-based ANN (DiskANN) Q6 - Multimodal embeddings & the modality gap Q7 - Collapse, whitening & PCA Q8 - Vector database internals Q9 - Streaming vs batch index builds Q10 - Cost & capacity planning Synthesis - the whole series in four ideas #vectordatabase #embeddings #diskann #systemdesign #mlengineering