Skill · AI Hiring Index · as of 2026-11-11
Agents in AI job postings
20% of technical postings at the AI companies we track mention Agents (1,017 postings). AI / ML Engineer postings ask for it most: 45%.
Technical postings that mention it
20%
1,017 postings
Change since the previous Index
−1 pt
Which roles ask for it
Share of each technical role family's open postings that mention Agents.
| Role family | Share, as a bar | Share |
|---|---|---|
| AI / ML Engineer | 45% | |
| Research Engineer | 35% | |
| FDE / Applied | 32% | |
| Research Scientist | 30% | |
| Security | 19% | |
| Software Eng | 17% | |
| Evals / Data | 16% | |
| Safety / Policy | 10% | |
| Infra / Hardware | 9% | |
| Inference / Perf | 4% |
Companies asking for it most
| Company | Postings | Of its technical postings |
|---|---|---|
| OpenAI | 115 | 23% |
| Anthropic | 87 | 24% |
| Sierra | 73 | 90% |
| Databricks | 72 | 13% |
| Decagon | 48 | 72% |
| Scale AI | 48 | 51% |
| Harvey | 43 | 48% |
| Mistral AI | 32 | 24% |
| Perplexity | 24 | 36% |
| Cohere | 24 | 35% |
How to learn it
- Building effective agents — Anthropic free
Workflows versus agents, and when you need neither: the handful of patterns these systems are built from, named by a lab that ships them.
- Hugging Face Agents Course free
Hands-on: tool calling, a ReAct-style loop and multi-agent setups in code you run — the gap between reading about agents and writing one.
- Effective context engineering for AI agents — Anthropic free
Context is the agent loop's most expensive variable: when to trim, compress, take notes or hand off to a sub-agent, sorted by problem — the checklist for your own loop's context budget.
Project: Write the agent loop yourself, then evaluate it — Messages API tool use with a step cap, tool-error retries and context trimming; score it on 30 real tasks for tool choice, argument correctness and steps taken.