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generative models jobs in 2026 — demand, top roles hiring, and related skills

As of 2026-09-30, generative models appears in 263 job postings indexed by Skillenai over the past 90 days — Machine Learning Engineer has the most postings mentioning generative models, with demand share down 3.6% vs the prior 4 weeks.

Last updated · 90d ending 2026-09-30

Postings · last 90 days
263
Demand vs prior month
down 3.6% vs the prior 4 weeks
Top role · 16.3% of skill postings
Machine Learning Engineer
Top hiring metro
San Francisco

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Frequently asked questions about generative models

+Is generative models in demand in 2026?

Yes. generative models appears in 263 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Machine Learning Engineer accounts for the most postings mentioning generative models (16.3% of all postings mentioning generative models).

+What jobs require generative models?

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 generative models are Applied Research Scientist (15.4% of that role’s postings mention generative models), Machine Learning Research Engineer (8.8% of that role’s postings mention generative models), Machine Learning Researcher (7.9% of that role’s postings mention generative models).

+What skills are commonly paired with generative models?

Across job postings indexed by Skillenai (90 days ending 2026-09-30), generative models most often appears alongside Python, machine learning, PyTorch, deep learning, TensorFlow.

+Where is generative models most in demand?

As of 2026-09-30, the metro areas posting the most jobs requiring generative models are San Francisco, London, New York City, Mountain View, San Jose, according to the Skillenai jobs index.

+How can I keep up with new generative models content and jobs?

Skillenai indexes news, blog posts, and research papers mentioning generative models alongside the jobs index. You can subscribe to a daily email digest of new generative models content from your Skillenai account.

+Which skills come before and after generative models?

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 generative models — last 90 days

Salary distribution

Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized

Career paths around generative models

Skills documented before and after this skill across employer changes.

Historical career profiles · all locations

Skills before generative models

Before generative modelspytorch → generative models: 2 observed employer moves with this skill pairfeature extraction → generative models: 1 observed employer moves with this skill pairJust Train Twice (JTT) → generative models: 1 observed employer moves with this skill pairpython → generative models: 1 observed employer moves with this skill pairnatural images → generative models: 1 observed employer moves with this skill pairNLP techniques → generative models: 1 observed employer moves with this skill pairclassifier models → generative models: 1 observed employer moves with this skill pairdenoising → generative models: 1 observed employer moves with this skill pairgenerativemodelspytorch: 2 movespytorch2 movesfeature extraction: 1 movesfeature extraction1 movesJust Train Twice (JTT): 1 movesJust Train Twice(JTT)1 movespython: 1 movespython1 movesnatural images: 1 movesnatural images1 movesNLP techniques: 1 movesNLP techniques1 movesclassifier models: 1 movesclassifier models1 movesdenoising: 1 movesdenoising1 moves

Skills after generative models

After generative modelsgenerative models → diverse outputs: 1 observed employer moves with this skill pairgenerative models → data profiling: 1 observed employer moves with this skill pairgenerative models → discriminator: 1 observed employer moves with this skill pairgenerative models → Natural Language Generation: 1 observed employer moves with this skill pairgenerative models → data science field: 1 observed employer moves with this skill pairgenerative models → DeepEval: 1 observed employer moves with this skill pairgenerative models → fine-tuning GANs: 1 observed employer moves with this skill pairgenerative models → data visualizations: 1 observed employer moves with this skill pairgenerativemodelsdiverse outputs: 1 movesdiverse outputs1 movesdata profiling: 1 movesdata profiling1 movesdiscriminator: 1 movesdiscriminator1 movesNatural Language Generation: 1 movesNatural LanguageGeneration1 movesdata science field: 1 movesdata science field1 movesDeepEval: 1 movesDeepEval1 movesfine-tuning GANs: 1 movesfine-tuning GANs1 movesdata visualizations: 1 movesdatavisualizations1 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: pytorch2
Before: feature extraction1
Before: Just Train Twice (JTT)1
Before: python1
Before: natural images1
Before: NLP techniques1
Before: classifier models1
Before: denoising1
After: diverse outputs1
After: data profiling1
After: discriminator1
After: Natural Language Generation1
After: data science field1
After: DeepEval1
After: fine-tuning GANs1
After: data visualizations1

Roles most likely to require generative models

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

RolePostings mentioning skill% of role postings mentioning skill
Applied Research Scientist415.4%
Machine Learning Research Engineer38.8%
Machine Learning Researcher37.9%
AI Application Engineer26.9%
Forward Deployed Software Engineer36.0%
Applied Scientist145.6%
Mission Software Engineer34.8%
Creative Technologist14.8%
Postdoctoral Researcher14.8%
AI Solutions Engineer14.3%

Roles with the most generative models postings

RolePostings mentioning skillShare of skill postings
Machine Learning Engineer4316.3%
Forward Deployed Engineer207.6%
ML Engineer197.2%
Research Scientist155.7%
Applied Scientist145.3%
Software Engineer145.3%
AI Engineer72.7%
Data Scientist62.3%
Research Engineer62.3%
AI Researcher51.9%

Top companies posting jobs requiring generative models

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

Top companies posting jobs requiring generative models
CompanyPostings · 90 days
Waymo19
OpenAI16
Scale AI16
CLERA13
Qualtrics9
Torcrobotics6
PhysicsX6
Snap6
Spotify4
Cartesia4

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 generative models

NamePostingsShare
San Francisco269.9%
London186.8%
New York City155.7%
Mountain View124.6%
San Jose93.4%
Seattle83.0%
Paris51.9%
Reston51.9%
Toronto51.9%

Skills commonly paired with generative models

Python (153)machine learning (119)PyTorch (98)deep learning (76)TensorFlow (68)LLMs (63)computer vision (51)Reinforcement learning (49)JAX (43)diffusion models (38)

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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 generative models by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s generative models postings by all generative models 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 2,595 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
69a6bd2b1979de9c
data_as_of
2026-09-30
window_days
90
Hiring engineers who use generative models?

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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Compiled by Jared Rand · Data sourced from the Skillenai labor market index