LLM fine-tuning jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-29, LLM fine-tuning appears in 233 job postings indexed by Skillenai over the past 90 days — Machine Learning Engineer has the most postings mentioning LLM fine-tuning, with demand share up 2.6% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-29
Which roles want LLM fine-tuning?
Upload your resume and Skillenai will show which roles your LLM fine-tuning experience fits, which skills you already cover, and what is missing.
Frequently asked questions about LLM fine-tuning
+Is LLM fine-tuning in demand in 2026?
Yes. LLM fine-tuning appears in 233 job postings indexed by Skillenai over the 90 days ending 2026-10-29. Machine Learning Engineer accounts for the most postings mentioning LLM fine-tuning (15.9% of all postings mentioning LLM fine-tuning).
+What jobs require LLM fine-tuning?
According to the Skillenai jobs index over the 90 days ending 2026-10-29, among roles with at least 20 postings, the highest shares mentioning LLM fine-tuning are Field Application Engineer (15.0% of that role’s postings mention LLM fine-tuning), Agent Engineer (7.7% of that role’s postings mention LLM fine-tuning), Applied Data Scientist (7.4% of that role’s postings mention LLM fine-tuning).
+What skills are commonly paired with LLM fine-tuning?
Across job postings indexed by Skillenai (90 days ending 2026-10-29), LLM fine-tuning most often appears alongside Python, PyTorch, Machine Learning, prompt engineering, Generative AI.
+Where is LLM fine-tuning most in demand?
As of 2026-10-29, the metro areas posting the most jobs requiring LLM fine-tuning are San Francisco, Bengaluru, Seattle, Singapore, New York City, according to the Skillenai jobs index.
+How can I keep up with new LLM fine-tuning content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning LLM fine-tuning alongside the jobs index. You can subscribe to a daily email digest of new LLM fine-tuning content from your Skillenai account.
+Which skills come before and after LLM 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 LLM fine-tuning — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around LLM fine-tuning
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before LLM fine-tuning
Skills after LLM fine-tuning
No published outgoing skill pairs yet.
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: GenAI | 1 |
| Before: multilingual datasets | 1 |
| Before: multi-agent LLM framework | 1 |
| Before: ci/cd | 1 |
| Before: a/b testing | 1 |
| Before: python | 1 |
| Before: Quality Assurance | 1 |
| Before: Data ingestion | 1 |
Roles most likely to require LLM fine-tuning
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Field Application Engineer | 3 | 15.0% |
| Agent Engineer | 2 | 7.7% |
| Applied Data Scientist | 2 | 7.4% |
| AI Research Intern | 2 | 6.5% |
| Applied AI Scientist | 2 | 6.1% |
| Machine Learning Manager | 1 | 4.5% |
| Product Management Director | 1 | 4.5% |
| AI Ops Engineer | 1 | 3.8% |
| Sales Director | 1 | 2.9% |
| ML Research Engineer | 1 | 2.6% |
Roles with the most LLM fine-tuning postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Machine Learning Engineer | 37 | 15.9% |
| Data Scientist | 23 | 9.9% |
| AI Engineer | 22 | 9.4% |
| Software Engineer | 22 | 9.4% |
| ML Engineer | 15 | 6.4% |
| Conversational AI Developer | 11 | 4.7% |
| Research Scientist | 6 | 2.6% |
| Research Engineer | 5 | 2.1% |
| Solutions Architect | 5 | 2.1% |
| Machine Learning Research Scientist | 4 | 1.7% |
Top companies posting jobs requiring LLM fine-tuning
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| PwC | 12 |
| Airbnb | 9 |
| NVIDIA | 8 |
| ServiceNow | 7 |
| HotelTonight | 5 |
| Accenture | 5 |
| Barclays | 4 |
| Doctolib | 4 |
| Stripe | 4 |
| Extreme Networks | 4 |
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 LLM fine-tuning
| Name | Postings | Share |
|---|---|---|
| San Francisco | 11 | 4.7% |
| Bengaluru | 7 | 3.0% |
| Seattle | 7 | 3.0% |
| Singapore | 7 | 3.0% |
| New York City | 6 | 2.6% |
| Paris | 5 | 2.1% |
| Pune | 5 | 2.1% |
| San Jose | 5 | 2.1% |
| Palo Alto | 4 | 1.7% |
Skills commonly paired with LLM fine-tuning
Explore related pages
How this was computed
Weekly job-count charts omit the incomplete current week (weeks start Monday, UTC). Totals include all postings in the stated window. Counts derive from the Skillenai jobs index over the 90 days ending 2026-10-29. 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 LLM 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 LLM fine-tuning postings by all LLM 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 2,482 identified employers and covers 71% of earlier and 70% of latest indexed postings. Windows: 2026-06-30 to 2026-09-28 and 2026-07-28 to 2026-10-26 (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
- cda42045c857f499
- data_as_of
- 2026-10-29
- window_days
- 90