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

As of 2026-09-28, benchmarking appears in 1,165 job postings indexed by Skillenai over the past 90 days — Software Engineer has the most postings mentioning benchmarking, with demand down 34% vs the prior 4 weeks.

Last updated · 90d ending 2026-09-28

Postings · last 90 days
1,165
Demand vs prior month
down 34% vs the prior 4 weeks
Top role · 16.1% of skill postings
Top hiring metro
San Francisco

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Frequently asked questions about benchmarking

+Is benchmarking in demand in 2026?

Yes. benchmarking appears in 1,165 job postings indexed by Skillenai over the 90 days ending 2026-09-28, with demand down 34% vs the prior 4 weeks. Software Engineer accounts for the most postings mentioning benchmarking (16.1% of all postings mentioning benchmarking).

+What jobs require benchmarking?

According to the Skillenai jobs index over the 90 days ending 2026-09-28, among roles with at least 20 postings, the highest shares mentioning benchmarking are Technical Services Engineer (32.3% of that role’s postings mention benchmarking), Performance Test Engineer (20.0% of that role’s postings mention benchmarking), Performance Engineer (19.2% of that role’s postings mention benchmarking).

+What skills are commonly paired with benchmarking?

Across job postings indexed by Skillenai (90 days ending 2026-09-28), benchmarking most often appears alongside Python, C++, profiling, machine learning, Distributed systems.

+Where is benchmarking most in demand?

As of 2026-09-28, the metro areas posting the most jobs requiring benchmarking are San Francisco, New York City, London, Santa Clara, San Jose, according to the Skillenai jobs index.

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

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

Weekly job postings requiring benchmarking — last 90 days

Salary distribution

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

Roles most likely to require benchmarking

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

RolePostings mentioning skill% of role postings mentioning skill
Technical Services Engineer1032.3%
Performance Test Engineer620.0%
Performance Engineer1019.2%
Forward Deployment Engineer615.4%
Consulting Engineer914.5%
Research Fellow313.0%
ML Research Engineer612.5%
AI Systems Engineer511.6%
Machine Learning Systems Engineer311.1%
IT Consultant29.5%

Roles with the most benchmarking postings

RolePostings mentioning skillShare of skill postings
Software Engineer18816.1%
Product Manager393.3%
Data Scientist363.1%
Engineering Manager282.4%
Machine Learning Engineer282.4%
Research Engineer272.3%
Research Scientist221.9%
AI Engineer201.7%
Backend Engineer171.5%
ML Engineer151.3%

Top metros hiring for benchmarking

NamePostingsShare
San Francisco1129.6%
New York City443.8%
London413.5%
Santa Clara262.2%
San Jose191.6%
Toronto191.6%
Bengaluru181.5%
Dublin151.3%
Munich151.3%

Skills commonly paired with benchmarking

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How this was computed

Counts derive from the Skillenai jobs index over the 90 days ending 2026-09-28. 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 benchmarking by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s benchmarking postings by all benchmarking 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).

source
Skillenai jobs index, deduplicated daily
entity_id
334294c174d3bffd
data_as_of
2026-09-28
window_days
90
Hiring engineers who use benchmarking?

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