Skill · AI Hiring Index · as of 2026-10-05
RAG / retrieval in AI job postings
7% of technical postings at the AI companies we track mention RAG / retrieval (372 postings). AI / ML Engineer postings ask for it most: 36%.
Technical postings that mention it
7%
372 postings
Which roles ask for it
Share of each technical role family's open postings that mention RAG / retrieval.
| Role family | Share, as a bar | Share |
|---|---|---|
| AI / ML Engineer | 36% | |
| FDE / Applied | 16% | |
| Research Engineer | 8% | |
| Research Scientist | 6% | |
| Software Eng | 5% | |
| Evals / Data | 2% | |
| Infra / Hardware | 2% | |
| Security | 1% | |
| Safety / Policy | 0% | |
| Inference / Perf | 0% |
Companies asking for it most
| Company | Postings | Of its technical postings |
|---|---|---|
| Sierra | 54 | 73% |
| Databricks | 41 | 7% |
| OpenAI | 34 | 7% |
| Scale AI | 25 | 24% |
| Harvey | 24 | 24% |
| Mistral AI | 23 | 19% |
| Nebius | 20 | 9% |
| Exa | 16 | 64% |
| Cohere | 14 | 18% |
| Cresta | 14 | 32% |
How to learn it
- Introducing Contextual Retrieval — Anthropic free
Hybrid search (embeddings plus BM25), reranking and chunk context, each with a measured drop in retrieval failures — design with numbers.
Project: Hybrid retrieval with a labelled query set — BM25 plus vectors fused with reciprocal rank fusion, a reranker, and 50 hand-labelled queries; report recall@k and nDCG against keyword search alone.