Search & Retrieval Engineer
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- Build reliable retrieval foundations and a measurable relevance baseline
- Combine retrieval methods and prove quality with evaluation
- Scale search quality, freshness and performance across production systems
Benchmark: OpenAI + Anthropic + Apple search-engineering roles (Sep 2026): build retrieval/search algorithms across document, enterprise, knowledge and web-scale search; query understanding, indexing, lexical/vector retrieval, ranking/reranking, contextual answer generation, relevance evaluation, experimentation, freshness/latency and production-scale services. Mastery ladder: 2 job-market competencies per level; 2 direct VIDEO bridges per competency (12 total).
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Enrichment
Build reliable retrieval foundations and a measurable relevance baseline
Query understanding, lexical relevance + semantic retrieval
Coverage: Search & AI Search / NLP / Information Retrieval / Query Understanding
Embeddings, vector search + approximate nearest neighbors
Coverage: Embeddings / Vector Databases / ANN / Indexing
Combine retrieval methods and prove quality with evaluation
Hybrid retrieval, fusion + learning-to-rank / reranking
Coverage: BM25 / Vector Search / RRF / Reranking / Learning to Rank
RAG retrieval pipelines, grounding + retrieval evaluation
Coverage: RAG / Knowledge Retrieval / Grounding / Evals / Experimentation
Scale search quality, freshness and performance across production systems
Distributed indices, ANN architecture + latency/freshness tradeoffs
Coverage: Distributed Systems / FAISS / HNSW / Search Infrastructure / SLOs
Search quality metrics, online experiments + product feedback loops
Coverage: Precision / Recall / MRR / NDCG / A-B Testing / Product Analytics
Role-Readiness Proof / Capstone
Build a production search system for enterprise knowledge or a product catalog: data/index design, lexical baseline, embeddings/vector index, hybrid retrieval, reranker/LTR, permission-aware filters, query rewriting, an eval/judgment set with precision, recall, MRR and NDCG, latency/freshness SLOs, observability, an A/B experiment plan, failure analysis, and a retrieval-backed answer layer.
Proof Rubric
- Separate retrieval from generation quality
- establish baselines
- justify index, retrieval and ranking choices
- prove relevance with offline and online evidence
- handle permissions, freshness and scale
- quantify latency-cost-quality tradeoffs
- diagnose long-tail and query failures
- turn search behavior into a disciplined improvement loop