evaluation harnesses jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-31, evaluation harnesses appears in 286 job postings indexed by Skillenai over the past 90 days — Software Engineer has the most postings mentioning evaluation harnesses, with demand share up 10.7% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-31
Which roles want evaluation harnesses?
Upload your resume and Skillenai will show which roles your evaluation harnesses experience fits, which skills you already cover, and what is missing.
Frequently asked questions about evaluation harnesses
+Is evaluation harnesses in demand in 2026?
Yes. evaluation harnesses appears in 286 job postings indexed by Skillenai over the 90 days ending 2026-10-31. Software Engineer accounts for the most postings mentioning evaluation harnesses (14.3% of all postings mentioning evaluation harnesses).
+What jobs require evaluation harnesses?
According to the Skillenai jobs index over the 90 days ending 2026-10-31, among roles with at least 20 postings, the highest shares mentioning evaluation harnesses are Agentic AI Engineer (7.1% of that role’s postings mention evaluation harnesses), AI Data Engineer (6.2% of that role’s postings mention evaluation harnesses), Applied AI Engineer (5.8% of that role’s postings mention evaluation harnesses).
+What skills are commonly paired with evaluation harnesses?
Across job postings indexed by Skillenai (90 days ending 2026-10-31), evaluation harnesses most often appears alongside Python, observability, TypeScript, CI/CD, prompt engineering.
+Where is evaluation harnesses most in demand?
As of 2026-10-31, the metro areas posting the most jobs requiring evaluation harnesses are San Francisco, New York City, London, Munich, Atlanta, according to the Skillenai jobs index.
+How can I keep up with new evaluation harnesses content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning evaluation harnesses alongside the jobs index. You can subscribe to a daily email digest of new evaluation harnesses content from your Skillenai account.
+Which skills come before and after evaluation harnesses?
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 harnesses — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around evaluation harnesses
Skills documented before and after this skill across employer changes.
Not enough linked career history to draw this chart yet.
Roles most likely to require evaluation harnesses
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Agentic AI Engineer | 4 | 7.1% |
| AI Data Engineer | 4 | 6.2% |
| Applied AI Engineer | 25 | 5.8% |
| ML Research Engineer | 2 | 5.7% |
| Founding AI Engineer | 1 | 4.8% |
| Lead Architect | 1 | 4.5% |
| ML Systems Engineer | 1 | 4.3% |
| Postdoctoral Researcher | 1 | 4.3% |
| Lecturer | 1 | 3.6% |
| AI Research Engineer | 2 | 2.9% |
Roles with the most evaluation harnesses postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Software Engineer | 41 | 14.3% |
| AI Engineer | 34 | 11.9% |
| Applied AI Engineer | 25 | 8.7% |
| Machine Learning Engineer | 15 | 5.2% |
| Forward Deployed Engineer | 14 | 4.9% |
| Research Engineer | 10 | 3.5% |
| Product Manager | 8 | 2.8% |
| AI Platform Engineer | 6 | 2.1% |
| Research Scientist | 6 | 2.1% |
| ML Engineer | 5 | 1.7% |
Top companies posting jobs requiring evaluation harnesses
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| OpenAI | 9 |
| CLERA | 8 |
| Accenture | 6 |
| Teraco | 5 |
| SentinelOne | 5 |
| Scale AI | 4 |
| IFS | 4 |
| Twilio | 3 |
| Mastercard | 3 |
| Sonatype | 3 |
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 harnesses
| Name | Postings | Share |
|---|---|---|
| San Francisco | 23 | 8.0% |
| New York City | 15 | 5.2% |
| London | 13 | 4.5% |
| Munich | 4 | 1.4% |
| Atlanta | 3 | 1.0% |
| Menlo Park | 3 | 1.0% |
| Mountain View | 3 | 1.0% |
| Prague | 3 | 1.0% |
| Pune | 3 | 1.0% |
Skills commonly paired with evaluation harnesses
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 2026-10-31. 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 harnesses by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s evaluation harnesses postings by all evaluation harnesses 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 2,482 identified employers and covers 71% of earlier and 70% of latest indexed postings. Windows: 2026-06-30 to 2026-09-28 and 2026-07-28 to 2026-10-26 (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
- 8d869fc290cd8dfe
- data_as_of
- 2026-10-31
- window_days
- 90