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

As of 2026-09-30, machine learning models appears in 219 job postings indexed by Skillenai over the past 90 days — Data Scientist has the most postings mentioning machine learning models, with demand share up 1.4% vs the prior 4 weeks.

Last updated · 90d ending 2026-09-30

Postings · last 90 days
219
Demand vs prior month
up 1.4% vs the prior 4 weeks
Top role · 15.1% of skill postings
Data Scientist
Top hiring metro
New York City

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Frequently asked questions about machine learning models

+Is machine learning models in demand in 2026?

Yes. machine learning models appears in 219 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Data Scientist accounts for the most postings mentioning machine learning models (15.1% of all postings mentioning machine learning models).

+What jobs require machine learning 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 machine learning models are AI Intern (4.2% of that role’s postings mention machine learning models), Product Operations Manager (3.8% of that role’s postings mention machine learning models), Detection Engineer (3.6% of that role’s postings mention machine learning models).

+What skills are commonly paired with machine learning models?

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

+Where is machine learning models most in demand?

As of 2026-09-30, the metro areas posting the most jobs requiring machine learning models are New York City, San Francisco, Toronto, Bengaluru, London, according to the Skillenai jobs index.

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

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

+Which skills come before and after machine learning 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 machine learning models — last 90 days

Salary distribution

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

Career paths around machine learning models

Skills documented before and after this skill across employer changes.

Historical career profiles · all locations

Skills before machine learning models

Before machine learning modelspython → machine learning models: 61 observed employer moves with this skill pairsql → machine learning models: 39 observed employer moves with this skill pairTableau → machine learning models: 31 observed employer moves with this skill pairPower BI → machine learning models: 20 observed employer moves with this skill pairpandas → machine learning models: 15 observed employer moves with this skill pairnumpy → machine learning models: 11 observed employer moves with this skill pairPySpark → machine learning models: 10 observed employer moves with this skill pairmachine learning algorithms → machine learning models: 9 observed employer moves with this skill pairmachinelearningmodelspython: 61 movespython61 movessql: 39 movessql39 movesTableau: 31 movesTableau31 movesPower BI: 20 movesPower BI20 movespandas: 15 movespandas15 movesnumpy: 11 movesnumpy11 movesPySpark: 10 movesPySpark10 movesmachine learning algorithms: 9 movesmachine learningalgorithms9 moves

Skills after machine learning models

After machine learning modelsmachine learning models → python: 26 observed employer moves with this skill pairmachine learning models → Tableau: 25 observed employer moves with this skill pairmachine learning models → sql: 21 observed employer moves with this skill pairmachine learning models → AWS: 19 observed employer moves with this skill pairmachine learning models → Power BI: 17 observed employer moves with this skill pairmachine learning models → machine learning: 14 observed employer moves with this skill pairmachine learning models → docker: 12 observed employer moves with this skill pairmachine learning models → snowflake: 12 observed employer moves with this skill pairmachinelearningmodelspython: 26 movespython26 movesTableau: 25 movesTableau25 movessql: 21 movessql21 movesAWS: 19 movesAWS19 movesPower BI: 17 movesPower BI17 movesmachine learning: 14 movesmachine learning14 movesdocker: 12 movesdocker12 movessnowflake: 12 movessnowflake12 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: python61
Before: sql39
Before: Tableau31
Before: Power BI20
Before: pandas15
Before: numpy11
Before: PySpark10
Before: machine learning algorithms9
After: python26
After: Tableau25
After: sql21
After: AWS19
After: Power BI17
After: machine learning14
After: docker12
After: snowflake12

Roles most likely to require machine learning models

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

RolePostings mentioning skill% of role postings mentioning skill
AI Intern14.2%
Product Operations Manager13.8%
Detection Engineer13.6%
Data & AI Engineer13.4%
Technical Lead Manager13.4%
Data Science Director23.4%
DevOps Platform Engineer13.2%
Finance Analyst13.1%
Developer Relations Manager12.9%
Senior Data Engineer12.8%

Roles with the most machine learning models postings

RolePostings mentioning skillShare of skill postings
Data Scientist3315.1%
Software Engineer3114.2%
Machine Learning Engineer2611.9%
Product Manager209.1%
Data Engineer135.9%
Data Analyst52.3%
ML Engineer41.8%
Computer Vision & Machine Learning Engineer31.4%
Engineering Manager31.4%
Group Product Manager31.4%

Top companies posting jobs requiring machine learning models

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

Top companies posting jobs requiring machine learning models
CompanyPostings · 90 days
Barclays16
Stripe12
Wise4
Snap4
Cisco4
Google3
Tide3
Disney3
Novartis3
Airbnb3

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 machine learning models

NamePostingsShare
New York City115.0%
San Francisco94.1%
Toronto83.7%
Bengaluru73.2%
London73.2%
Pune52.3%
Amsterdam41.8%
Austin41.8%
Berlin41.8%

Skills commonly paired with machine learning models

Python (109)SQL (63)data pipelines (52)PyTorch (42)TensorFlow (40)Data analysis (39)AWS (35)R (26)machine learning (24)scikit-learn (20)

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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 machine learning models by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s machine learning models postings by all machine learning 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
a1e1bf6f68e68425
data_as_of
2026-09-30
window_days
90
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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