evaluation frameworks jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-09-30, evaluation frameworks appears in 1,755 job postings indexed by Skillenai over the past 90 days — Technical Product Lead has the most postings mentioning evaluation frameworks.
Last updated · 90d ending 2026-09-30
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Frequently asked questions about evaluation frameworks
+Is evaluation frameworks in demand in 2026?
Yes. evaluation frameworks appears in 1,755 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Technical Product Lead accounts for the most postings mentioning evaluation frameworks (15.8% of all postings mentioning evaluation frameworks).
+What jobs require evaluation frameworks?
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 evaluation frameworks are Technical Product Lead (97.9% of that role’s postings mention evaluation frameworks), Agent Strategist (69.0% of that role’s postings mention evaluation frameworks), Applied AI Architect (53.9% of that role’s postings mention evaluation frameworks).
+What skills are commonly paired with evaluation frameworks?
Across job postings indexed by Skillenai (90 days ending 2026-09-30), evaluation frameworks most often appears alongside machine learning, LLMs, Distributed systems, Python, APIs.
+Where is evaluation frameworks most in demand?
As of 2026-09-30, the metro areas posting the most jobs requiring evaluation frameworks are San Francisco, New York City, Singapore, London, San Jose, according to the Skillenai jobs index.
+How can I keep up with new evaluation frameworks content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning evaluation frameworks alongside the jobs index. You can subscribe to a daily email digest of new evaluation frameworks content from your Skillenai account.
+Which skills come before and after evaluation frameworks?
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 evaluation frameworks — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around evaluation frameworks
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before evaluation frameworks
Skills after evaluation frameworks
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 | 2 |
| Before: dashboards | 2 |
| Before: Digital cards | 1 |
| Before: Weill Cornell | 1 |
| Before: reporting systems | 1 |
| Before: scikit-learn | 1 |
| Before: testing | 1 |
| Before: visualizations | 1 |
| After: Design rationale presentation | 1 |
| After: strategic plans | 1 |
| After: wearable biometrics | 1 |
| After: computer vision | 1 |
| After: board reports | 1 |
| After: Training and fine-tuning | 1 |
| After: healthcare systems | 1 |
| After: Accelerometer | 1 |
Roles most likely to require evaluation frameworks
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Technical Product Lead | 277 | 97.9% |
| Agent Strategist | 20 | 69.0% |
| Applied AI Architect | 41 | 53.9% |
| Applied Research Engineer | 9 | 34.6% |
| Technical Product Manager | 223 | 22.0% |
| Agentic AI Engineer | 11 | 17.7% |
| AI Data Scientist | 3 | 15.0% |
| Agent Engineer | 3 | 14.3% |
| AI Platform Architect | 3 | 13.6% |
| Software Development Test Engineer | 4 | 13.3% |
Roles with the most evaluation frameworks postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Technical Product Lead | 277 | 15.8% |
| Technical Product Manager | 223 | 12.7% |
| Software Engineer | 173 | 9.9% |
| AI Engineer | 109 | 6.2% |
| Product Manager | 97 | 5.5% |
| Machine Learning Engineer | 56 | 3.2% |
| Data Scientist | 51 | 2.9% |
| Applied AI Engineer | 43 | 2.5% |
| Applied AI Architect | 41 | 2.3% |
| Forward Deployed Engineer | 31 | 1.8% |
Top companies posting jobs requiring evaluation frameworks
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Bjakcareer | 514 |
| Anthropic | 94 |
| CLERA | 62 |
| Scale AI | 32 |
| Waymo | 22 |
| Sierra | 17 |
| ServiceNow | 16 |
| Spotify | 15 |
| Cisco | 14 |
| Adobe | 13 |
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 evaluation frameworks
| Name | Postings | Share |
|---|---|---|
| San Francisco | 137 | 7.8% |
| New York City | 125 | 7.1% |
| Singapore | 50 | 2.8% |
| London | 46 | 2.6% |
| San Jose | 28 | 1.6% |
| Seoul | 28 | 1.6% |
| Sydney | 27 | 1.5% |
| Seattle | 25 | 1.4% |
| Bengaluru | 23 | 1.3% |
Skills commonly paired with evaluation frameworks
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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 evaluation frameworks by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s evaluation frameworks postings by all evaluation frameworks 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). An adjusted trend is not shown because comparable posting coverage is insufficient.
- source
- Skillenai jobs index, deduplicated daily
- entity_id
- 729739e49d3dc46d
- data_as_of
- 2026-09-30
- window_days
- 90
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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