Skill · AI Hiring Index · as of 2026-10-05
Fine-tuning in AI job postings
4% of technical postings at the AI companies we track mention Fine-tuning (189 postings). AI / ML Engineer postings ask for it most: 16%.
Technical postings that mention it
4%
189 postings
Which roles ask for it
Share of each technical role family's open postings that mention Fine-tuning.
| Role family | Share, as a bar | Share |
|---|---|---|
| AI / ML Engineer | 16% | |
| Research Scientist | 15% | |
| Research Engineer | 15% | |
| FDE / Applied | 8% | |
| Inference / Perf | 2% | |
| Software Eng | 1% | |
| Evals / Data | 1% | |
| Infra / Hardware | 0% | |
| Safety / Policy | 0% | |
| Security | 0% |
Companies asking for it most
| Company | Postings | Of its technical postings |
|---|---|---|
| Databricks | 22 | 4% |
| Mistral AI | 21 | 18% |
| Scale AI | 13 | 12% |
| Fireworks AI | 13 | 42% |
| Nebius | 11 | 5% |
| CoreWeave | 9 | 5% |
| Together AI | 9 | 16% |
| Anthropic | 7 | 2% |
| Liquid AI | 7 | 50% |
| Thinking Machines Lab | 7 | 16% |
How to learn it
- Hugging Face LLM Course free
Tokenizers, the Trainer, and fine-tuning chapters in runnable notebooks — enough to fine-tune a small open model on your own data.
- TRL documentation — Hugging Face free
The library behind most supervised fine-tuning and preference tuning (DPO, GRPO) you will be asked about, with working recipes.
Project: Fine-tune against a prompted baseline — LoRA-tune a small open model on one narrow task and compare it with a prompted frontier model on the same eval set — quality, latency and cost per 1,000 calls.