fine-tuning jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-09-30, fine-tuning appears in 2,075 job postings indexed by Skillenai over the past 90 days — AI Engineer has the most postings mentioning fine-tuning, with demand share down 1.6% vs the prior 4 weeks.
Last updated · 90d ending 2026-09-30
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Frequently asked questions about fine-tuning
+Is fine-tuning in demand in 2026?
Yes. fine-tuning appears in 2,075 job postings indexed by Skillenai over the 90 days ending 2026-09-30. AI Engineer accounts for the most postings mentioning fine-tuning (12.3% of all postings mentioning fine-tuning).
+What jobs require fine-tuning?
According to the Skillenai jobs index over the 90 days ending 2026-09-30, among roles with at least 20 postings, the highest shares mentioning fine-tuning are Generative AI Specialist (79.3% of that role’s postings mention fine-tuning), Applied Machine Learning Engineer (45.8% of that role’s postings mention fine-tuning), Applied Researcher (35.7% of that role’s postings mention fine-tuning).
+What skills are commonly paired with fine-tuning?
Across job postings indexed by Skillenai (90 days ending 2026-09-30), fine-tuning most often appears alongside Python, PyTorch, machine learning, prompt engineering, RAG.
+Where is fine-tuning most in demand?
As of 2026-09-30, the metro areas posting the most jobs requiring fine-tuning are San Francisco, New York City, London, Bengaluru, San Jose, according to the Skillenai jobs index.
+How can I keep up with new fine-tuning content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning fine-tuning alongside the jobs index. You can subscribe to a daily email digest of new fine-tuning content from your Skillenai account.
+Which skills come before and after fine-tuning?
The skill-flow chart shows skills documented in adjacent positions across observed employer changes. An outgoing skill is documented in the following position but not the preceding one. These are ideas to explore, not proven prerequisites, acquisition dates, or levels of mastery. Each ribbon counts employer moves with that skill pair; one move can contribute several pairs.
Weekly indexed postings requiring fine-tuning — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around fine-tuning
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before fine-tuning
Skills after fine-tuning
How to read this chart · view counts
Each side is an independent set of observed employer moves, not the same people followed through three stages. Ribbon widths compare move counts within that side. Internal moves are not included.
The following position documents a skill that the preceding position does not. Skills must be linked to both positions, with clear dates and no overlap. One move can connect several skill pairs. These patterns suggest skills to explore; they do not establish prerequisites, when a skill was learned, or a higher skill level.
Source: Skillenai talent graph, historical career profiles. Historical descriptions and coverage can change. Only the leading published connections are shown.
| Connection | Moves |
|---|---|
| Before: python | 10 |
| Before: ci/cd | 4 |
| Before: ETL | 3 |
| Before: docker | 3 |
| Before: sql | 3 |
| Before: AWS | 3 |
| Before: machine learning | 3 |
| Before: Jenkins | 3 |
| After: python | 3 |
| After: machine learning | 2 |
| After: Random Forest | 2 |
| After: terraform | 2 |
| After: prompt engineering | 2 |
| After: observability | 2 |
| After: AI | 2 |
| After: docker | 2 |
Roles most likely to require fine-tuning
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Generative AI Specialist | 46 | 79.3% |
| Applied Machine Learning Engineer | 11 | 45.8% |
| Applied Researcher | 15 | 35.7% |
| AI/ML Architect | 8 | 30.8% |
| Applied Value Engineer | 10 | 25.0% |
| Agent Architect | 8 | 22.9% |
| AI Research Scientist | 18 | 22.2% |
| Applied AI Scientist | 6 | 20.7% |
| Machine Learning Research Engineer | 7 | 20.6% |
| Gen AI Engineer | 4 | 20.0% |
Roles with the most fine-tuning postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| AI Engineer | 255 | 12.3% |
| Machine Learning Engineer | 218 | 10.5% |
| Software Engineer | 144 | 6.9% |
| Data Scientist | 99 | 4.8% |
| ML Engineer | 92 | 4.4% |
| Product Manager | 56 | 2.7% |
| Applied AI Engineer | 51 | 2.5% |
| Generative AI Specialist | 46 | 2.2% |
| AI/ML Engineer | 42 | 2.0% |
| Solutions Architect | 41 | 2.0% |
Top companies posting jobs requiring fine-tuning
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Innodata | 62 |
| Databricks | 54 |
| CLERA | 47 |
| Fireworks | 36 |
| Celonis | 32 |
| Cisco | 32 |
| Waymo | 31 |
| Scale AI | 26 |
| Capital One | 25 |
| Mistral | 25 |
Job postings indexed over the past 90 days, grouped by resolved employer. Counts are postings, not hires. Companies without a published page appear without a link.
Top metros hiring for fine-tuning
| Name | Postings | Share |
|---|---|---|
| San Francisco | 169 | 8.1% |
| New York City | 132 | 6.4% |
| London | 73 | 3.5% |
| Bengaluru | 50 | 2.4% |
| San Jose | 47 | 2.3% |
| Singapore | 44 | 2.1% |
| Mountain View | 39 | 1.9% |
| Seattle | 32 | 1.5% |
| Toronto | 32 | 1.5% |
Skills commonly paired with fine-tuning
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How this was computed
Counts derive from the Skillenai jobs index over the 90 days ending 2026-09-30. Skills are resolved against the Skillenai canonical taxonomy, so the same entity is counted whether a posting writes 'Python', 'Python 3', or 'python'. Role prevalence divides postings mentioning fine-tuning by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s fine-tuning postings by all fine-tuning postings, including postings without a role. Shares need not sum to 100% for the displayed roles. Pages refresh weekly (or daily for the top-50 most-requested skills). Adjusted posting share: 1.0% to 1.0%. Demand share change is the relative percentage change between these adjusted shares. Each employer-and-ATS group has at least 10 postings in each 90-day window; its earlier posting count supplies the same weight in both windows. The panel includes 2,596 identified employers and covers 68% of earlier and 72% of latest indexed postings. Windows: 2026-06-02 to 2026-08-31 and 2026-06-30 to 2026-09-28 (UTC; end dates excluded). The windows overlap by 62 days. Dates reflect indexing, not the employer’s posting date. This measures posting mix, not total hiring or market-wide demand. Matching excludes entrants and exits; changes in crawl completeness within an employer or ATS can still affect the result.
- source
- Skillenai jobs index, deduplicated daily
- entity_id
- bf6d5599efd19a5b
- data_as_of
- 2026-09-30
- window_days
- 90
The demand, skills, and geo numbers on this page come from the same Skillenai labor market index that powers our API. Use it for compensation benchmarking, hiring-competition analysis, and skill-adoption tracking.
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