model fine-tuning jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-19, model fine-tuning appears in 182 job postings indexed by Skillenai over the past 90 days — Software Engineer has the most postings mentioning model fine-tuning, with demand share up 6.4% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-19
Which roles want model fine-tuning?
Upload your resume and Skillenai will show which roles your model fine-tuning experience fits, which skills you already cover, and what is missing.
Frequently asked questions about model fine-tuning
+Is model fine-tuning in demand in 2026?
Yes. model fine-tuning appears in 182 job postings indexed by Skillenai over the 90 days ending 2026-10-19. Software Engineer accounts for the most postings mentioning model fine-tuning (12.6% of all postings mentioning model fine-tuning).
+What jobs require model fine-tuning?
According to the Skillenai jobs index over the 90 days ending 2026-10-19, among roles with at least 20 postings, the highest shares mentioning model fine-tuning are AI/ML Scientist (9.5% of that role’s postings mention model fine-tuning), AI Technical Lead (8.0% of that role’s postings mention model fine-tuning), AI Ops Engineer (7.7% of that role’s postings mention model fine-tuning).
+What skills are commonly paired with model fine-tuning?
Across job postings indexed by Skillenai (90 days ending 2026-10-19), model fine-tuning most often appears alongside Python, prompt engineering, machine learning, PyTorch, Generative AI.
+Where is model fine-tuning most in demand?
As of 2026-10-19, the metro areas posting the most jobs requiring model fine-tuning are Berlin, London, San Francisco, Bengaluru, Singapore, according to the Skillenai jobs index.
+How can I keep up with new model fine-tuning content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning model fine-tuning alongside the jobs index. You can subscribe to a daily email digest of new model fine-tuning content from your Skillenai account.
+Which skills come before and after model 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 model fine-tuning — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around model fine-tuning
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before model fine-tuning
Skills after model 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: Llama | 2 |
| Before: Environment Layer | 1 |
| Before: pre-training | 1 |
| Before: Ray | 1 |
| Before: NLP tasks | 1 |
| Before: Object-oriented programming | 1 |
| Before: GPT | 1 |
| Before: RAG pipelines | 1 |
| After: firewall configuration | 1 |
| After: news summarizer | 1 |
| After: Chrome Extension | 1 |
| After: transformer model | 1 |
| After: statics | 1 |
| After: decentralized network security policies | 1 |
| After: software patching | 1 |
| After: circuit analysis | 1 |
Roles most likely to require model fine-tuning
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| AI/ML Scientist | 2 | 9.5% |
| AI Technical Lead | 2 | 8.0% |
| AI Ops Engineer | 2 | 7.7% |
| Developer Experience Engineer | 2 | 7.4% |
| GenAI Engineer | 2 | 5.0% |
| Applied Machine Learning Engineer | 1 | 4.3% |
| AI Lead | 1 | 4.2% |
| AI Deployment Strategist | 2 | 4.1% |
| AI Engineering Director | 2 | 4.1% |
| AI Scientist | 3 | 3.5% |
Roles with the most model fine-tuning postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Software Engineer | 23 | 12.6% |
| Product Manager | 16 | 8.8% |
| Data Scientist | 15 | 8.2% |
| Machine Learning Engineer | 15 | 8.2% |
| AI Engineer | 14 | 7.7% |
| ML Engineer | 10 | 5.5% |
| Research Engineer | 5 | 2.7% |
| AI/ML Engineer | 4 | 2.2% |
| AI & LLM Solution Architect | 3 | 1.6% |
| AI Scientist | 3 | 1.6% |
Top companies posting jobs requiring model fine-tuning
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| CLERA | 10 |
| Grab | 8 |
| Veeva Systems | 5 |
| 4 | |
| Cisco | 4 |
| Anaplan | 4 |
| Calendly | 3 |
| Mistral | 3 |
| Tencent | 3 |
| Nexthink | 3 |
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 model fine-tuning
| Name | Postings | Share |
|---|---|---|
| Berlin | 10 | 5.5% |
| London | 7 | 3.8% |
| San Francisco | 7 | 3.8% |
| Bengaluru | 6 | 3.3% |
| Singapore | 6 | 3.3% |
| New York City | 5 | 2.7% |
| San Jose | 5 | 2.7% |
| Seattle | 3 | 1.6% |
| Seoul | 3 | 1.6% |
Skills commonly paired with model fine-tuning
Explore related pages
How this was computed
Counts derive from the Skillenai jobs index over the 90 days ending 2026-10-19. 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 model 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 model fine-tuning postings by all model 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: 0.1% to 0.1%. 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 2,611 identified employers and covers 72% of earlier and 73% of latest indexed postings. Windows: 2026-06-23 to 2026-09-21 and 2026-07-21 to 2026-10-19 (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
- ddf61c5e7be7695a
- data_as_of
- 2026-10-19
- window_days
- 90