Skillenai

fine-tuning jobs in 2026 — demand, top roles hiring, and related skills

As of 2026-11-04, fine-tuning appears in 2,355 job postings indexed by Skillenai over the past 90 days — AI Engineer has the most postings mentioning fine-tuning, with demand share up 0.5% vs the prior 4 weeks.

Last updated · 90d ending 2026-11-04

Postings · last 90 days
2,355
Demand vs prior month
up 0.5% vs the prior 4 weeks
Top role · 14.6% of skill postings
AI Engineer
Top hiring metro
San Francisco

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Frequently asked questions about fine-tuning

+Is fine-tuning in demand in 2026?

Yes. fine-tuning appears in 2,355 job postings indexed by Skillenai over the 90 days ending 2026-11-04. AI Engineer accounts for the most postings mentioning fine-tuning (14.6% of all postings mentioning fine-tuning).

+What jobs require fine-tuning?

According to the Skillenai jobs index over the 90 days ending 2026-11-04, among roles with at least 20 postings, the highest shares mentioning fine-tuning are AI Prompt Engineer (40.0% of that role’s postings mention fine-tuning), Applied Researcher (34.5% of that role’s postings mention fine-tuning), Agent Architect (33.3% 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 2026-11-04), fine-tuning most often appears alongside Python, PyTorch, prompt engineering, machine learning, RAG.

+Where is fine-tuning most in demand?

As of 2026-11-04, the metro areas posting the most jobs requiring fine-tuning are San Francisco, New York City, London, San Jose, Bengaluru, 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

Before fine-tuningpython → fine-tuning: 12 observed employer moves with this skill pairAWS → fine-tuning: 5 observed employer moves with this skill pairci/cd → fine-tuning: 4 observed employer moves with this skill pairmachine learning → fine-tuning: 4 observed employer moves with this skill pairdeep learning → fine-tuning: 4 observed employer moves with this skill pairETL → fine-tuning: 3 observed employer moves with this skill pairdocker → fine-tuning: 3 observed employer moves with this skill pairsql → fine-tuning: 3 observed employer moves with this skill pairfine-tuningpython: 12 movespython12 movesAWS: 5 movesAWS5 movesci/cd: 4 movesci/cd4 movesmachine learning: 4 movesmachine learning4 movesdeep learning: 4 movesdeep learning4 movesETL: 3 movesETL3 movesdocker: 3 movesdocker3 movessql: 3 movessql3 moves

Skills after fine-tuning

After fine-tuningfine-tuning → python: 3 observed employer moves with this skill pairfine-tuning → terraform: 2 observed employer moves with this skill pairfine-tuning → Random Forest: 2 observed employer moves with this skill pairfine-tuning → machine learning: 2 observed employer moves with this skill pairfine-tuning → prompt engineering: 2 observed employer moves with this skill pairfine-tuning → observability: 2 observed employer moves with this skill pairfine-tuning → AI: 2 observed employer moves with this skill pairfine-tuning → Tableau: 2 observed employer moves with this skill pairfine-tuningpython: 3 movespython3 movesterraform: 2 movesterraform2 movesRandom Forest: 2 movesRandom Forest2 movesmachine learning: 2 movesmachine learning2 movesprompt engineering: 2 movesprompt engineering2 movesobservability: 2 movesobservability2 movesAI: 2 movesAI2 movesTableau: 2 movesTableau2 moves
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.

Observed connections and move counts
ConnectionMoves
Before: python12
Before: AWS5
Before: ci/cd4
Before: machine learning4
Before: deep learning4
Before: ETL3
Before: docker3
Before: sql3
After: python3
After: terraform2
After: Random Forest2
After: machine learning2
After: prompt engineering2
After: observability2
After: AI2
After: Tableau2

Roles most likely to require fine-tuning

Among roles with at least 20 postings in the same period.

RolePostings mentioning skill% of role postings mentioning skill
AI Prompt Engineer840.0%
Applied Researcher1934.5%
Agent Architect733.3%
AI Research Engineer1824.3%
Applied Value Engineer724.1%
AI Research Scientist1924.1%
Generative AI Engineer1322.8%
AI Researcher2722.7%
Data Science Lead521.7%
Gen AI Engineer419.0%

Roles with the most fine-tuning postings

RolePostings mentioning skillShare of skill postings
AI Engineer34314.6%
Machine Learning Engineer2139.0%
Software Engineer1787.6%
ML Engineer1405.9%
Data Scientist1395.9%
Solutions Architect652.8%
Product Manager622.6%
Applied AI Engineer472.0%
Research Engineer472.0%
Research Scientist411.7%

Top companies posting jobs requiring fine-tuning

Employers ranked by indexed job postings in the last 90 days.

Top companies posting jobs requiring fine-tuning
CompanyPostings · 90 days
CLERA61
Databricks57
Cisco42
NVIDIA37
Capital One35
Mistral33
Accenture25
Scale AI24
Bosch24
Celonis22

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

NamePostingsShare
San Francisco2038.6%
New York City1124.8%
London672.8%
San Jose522.2%
Bengaluru512.2%
Munich311.3%
Palo Alto301.3%
Seattle291.2%
Singapore291.2%

Skills commonly paired with fine-tuning

Python (1,334)PyTorch (754)prompt engineering (711)machine learning (674)RAG (597)AWS (418)LLMs (404)LangChain (395)Generative AI (369)MLOps (351)

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 2026-11-04. 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 2,472 identified employers and covers 71% of earlier and 70% of latest indexed postings. Windows: 2026-07-07 to 2026-10-05 and 2026-08-04 to 2026-11-02 (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
2026-11-04
window_days
90
Compiled by Jared Rand · Data sourced from the Skillenai labor market index