model evaluation jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-11-05, model evaluation appears in 2,596 job postings indexed by Skillenai over the past 90 days — AI Engineer has the most postings mentioning model evaluation, with demand share down 0.6% vs the prior 4 weeks.
Last updated · 90d ending 2026-11-05
Which roles want model evaluation?
Upload your resume and Skillenai will show which roles your model evaluation experience fits, which skills you already cover, and what is missing.
Frequently asked questions about model evaluation
+Is model evaluation in demand in 2026?
Yes. model evaluation appears in 2,596 job postings indexed by Skillenai over the 90 days ending 2026-11-05. AI Engineer accounts for the most postings mentioning model evaluation (13.0% of all postings mentioning model evaluation).
+What jobs require model evaluation?
According to the Skillenai jobs index over the 90 days ending 2026-11-05, among roles with at least 20 postings, the highest shares mentioning model evaluation are ML Platform Engineer (31.2% of that role’s postings mention model evaluation), AI Engineering Director (20.0% of that role’s postings mention model evaluation), Deployment Strategist (19.8% 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 2026-11-05), model evaluation most often appears alongside Python, machine learning, PyTorch, prompt engineering, observability.
+Where is model evaluation most in demand?
As of 2026-11-05, the metro areas posting the most jobs requiring model evaluation are San Francisco, New York City, London, Singapore, 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
Skills after model evaluation
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: python | 11 |
| Before: sql | 6 |
| Before: scikit-learn | 4 |
| Before: pandas | 4 |
| Before: Tableau | 4 |
| Before: Power BI | 3 |
| Before: docker | 3 |
| Before: react.js | 3 |
| After: Apache Spark | 3 |
| After: docker | 3 |
| After: Pinecone | 2 |
| After: databricks | 2 |
| After: feature engineering | 2 |
| After: kubernetes | 2 |
| After: Java | 2 |
| After: Lambda | 2 |
Roles most likely to require model evaluation
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| ML Platform Engineer | 15 | 31.2% |
| AI Engineering Director | 10 | 20.0% |
| Deployment Strategist | 19 | 19.8% |
| Gen AI Engineer | 4 | 19.0% |
| Forward Deployed AI Engineer | 19 | 18.3% |
| Agent Engineer | 5 | 16.1% |
| Applied Data Scientist | 4 | 14.8% |
| Applied Scientist | 35 | 14.4% |
| AI Solutions Engineer | 3 | 14.3% |
| Machine Learning Systems Engineer | 3 | 14.3% |
Roles with the most model evaluation postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| AI Engineer | 337 | 13.0% |
| Machine Learning Engineer | 280 | 10.8% |
| Data Scientist | 265 | 10.2% |
| Software Engineer | 180 | 6.9% |
| ML Engineer | 126 | 4.9% |
| Product Manager | 107 | 4.1% |
| AI/ML Engineer | 42 | 1.6% |
| Research Engineer | 38 | 1.5% |
| Applied Scientist | 35 | 1.3% |
| Technical Program Manager | 32 | 1.2% |
Top companies posting jobs requiring model evaluation
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Capital One | 144 |
| OpenBrain | 84 |
| Bjakcareer | 74 |
| 50 | |
| CLERA | 45 |
| ServiceNow | 45 |
| Bosch | 29 |
| Highmetric | 27 |
| OpenAI | 26 |
| Elsevier | 24 |
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
| Name | Postings | Share |
|---|---|---|
| San Francisco | 179 | 6.9% |
| New York City | 95 | 3.7% |
| London | 77 | 3.0% |
| Singapore | 54 | 2.1% |
| Bengaluru | 42 | 1.6% |
| Santa Clara | 36 | 1.4% |
| Seattle | 31 | 1.2% |
| Mountain View | 30 | 1.2% |
| Boston | 29 | 1.1% |
Skills commonly paired with model evaluation
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 2026-11-05. 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: 1.2% to 1.2%. 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
- 46a0ad6dd7189597
- data_as_of
- 2026-11-05
- window_days
- 90