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model evaluation jobs in 2026 — demand, top roles hiring, and related skills

As of 2026-09-30, model evaluation appears in 2,439 job postings indexed by Skillenai over the past 90 days — Machine Learning Engineer has the most postings mentioning model evaluation, with demand share up 5.8% vs the prior 4 weeks.

Last updated · 90d ending 2026-09-30

Postings · last 90 days
2,439
Demand vs prior month
up 5.8% vs the prior 4 weeks
Top role · 12.9% of skill postings
Machine Learning Engineer
Top hiring metro
San Francisco

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Frequently asked questions about model evaluation

+Is model evaluation in demand in 2026?

Yes. model evaluation appears in 2,439 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Machine Learning Engineer accounts for the most postings mentioning model evaluation (12.9% of all postings mentioning model evaluation).

+What jobs require model evaluation?

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 model evaluation are AI Data Scientist (35.0% of that role’s postings mention model evaluation), ML Platform Engineer (34.0% of that role’s postings mention model evaluation), AI Response Evaluator (33.3% of that role’s postings mention model evaluation).

+What skills are commonly paired with model evaluation?

Across job postings indexed by Skillenai (90 days ending 2026-09-30), model evaluation most often appears alongside Python, machine learning, PyTorch, model training, SQL.

+Where is model evaluation most in demand?

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

+How can I keep up with new model evaluation content and jobs?

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

+Which skills come before and after model evaluation?

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 evaluation — last 90 days

Salary distribution

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

Career paths around model evaluation

Skills documented before and after this skill across employer changes.

Historical career profiles · all locations

Skills before model evaluation

Before model evaluationpython → model evaluation: 11 observed employer moves with this skill pairsql → model evaluation: 6 observed employer moves with this skill pairscikit-learn → model evaluation: 4 observed employer moves with this skill pairpandas → model evaluation: 4 observed employer moves with this skill pairTableau → model evaluation: 4 observed employer moves with this skill pairPower BI → model evaluation: 3 observed employer moves with this skill pairdocker → model evaluation: 3 observed employer moves with this skill pairreact.js → model evaluation: 3 observed employer moves with this skill pairmodelevaluationpython: 11 movespython11 movessql: 6 movessql6 movesscikit-learn: 4 movesscikit-learn4 movespandas: 4 movespandas4 movesTableau: 4 movesTableau4 movesPower BI: 3 movesPower BI3 movesdocker: 3 movesdocker3 movesreact.js: 3 movesreact.js3 moves

Skills after model evaluation

After model evaluationmodel evaluation → Apache Spark: 3 observed employer moves with this skill pairmodel evaluation → docker: 3 observed employer moves with this skill pairmodel evaluation → R: 2 observed employer moves with this skill pairmodel evaluation → exploratory data analysis: 2 observed employer moves with this skill pairmodel evaluation → data cleaning: 2 observed employer moves with this skill pairmodel evaluation → reports: 2 observed employer moves with this skill pairmodel evaluation → databricks: 2 observed employer moves with this skill pairmodel evaluation → feature engineering: 2 observed employer moves with this skill pairmodelevaluationApache Spark: 3 movesApache Spark3 movesdocker: 3 movesdocker3 movesR: 2 movesR2 movesexploratory data analysis: 2 movesexploratory dataanalysis2 movesdata cleaning: 2 movesdata cleaning2 movesreports: 2 movesreports2 movesdatabricks: 2 movesdatabricks2 movesfeature engineering: 2 movesfeatureengineering2 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: python11
Before: sql6
Before: scikit-learn4
Before: pandas4
Before: Tableau4
Before: Power BI3
Before: docker3
Before: react.js3
After: Apache Spark3
After: docker3
After: R2
After: exploratory data analysis2
After: data cleaning2
After: reports2
After: databricks2
After: feature engineering2

Roles most likely to require model evaluation

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

RolePostings mentioning skill% of role postings mentioning skill
AI Data Scientist735.0%
ML Platform Engineer1634.0%
AI Response Evaluator933.3%
Machine Learning Research Engineer1029.4%
Gen AI Engineer525.0%
Applied ML Engineer419.0%
Forward Deployed AI Engineer1818.9%
Forward Deployment Engineer717.1%
Machine Learning Engineering Manager1016.9%
Applied Machine Learning Engineer416.7%

Roles with the most model evaluation postings

RolePostings mentioning skillShare of skill postings
Machine Learning Engineer31512.9%
AI Engineer26210.7%
Data Scientist2429.9%
Software Engineer1807.4%
ML Engineer1054.3%
Product Manager933.8%
AI/ML Engineer411.7%
Research Engineer371.5%
Applied Scientist321.3%
Technical Product Manager291.2%

Top companies posting jobs requiring model evaluation

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

Top companies posting jobs requiring model evaluation
CompanyPostings · 90 days
Capital One113
OpenBrain108
Bjakcareer70
Google52
Waymo51
CLERA39
ServiceNow35
Scale AI32
Highmetric27
Bosch25

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 evaluation

NamePostingsShare
San Francisco1586.5%
New York City1184.8%
London953.9%
Mountain View522.1%
Bengaluru441.8%
Singapore441.8%
Seattle321.3%
Toronto321.3%
San Jose301.2%

Skills commonly paired with model evaluation

Python (1,280)machine learning (1,049)PyTorch (614)model training (453)SQL (374)prompt engineering (360)observability (338)feature engineering (337)TensorFlow (312)data pipelines (298)

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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 model evaluation by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s model evaluation postings by all model evaluation 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 2,596 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
46a0ad6dd7189597
data_as_of
2026-09-30
window_days
90
Hiring engineers who use model evaluation?

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