Domains / Machine learning & AI

Machine learning & AI, 3,200+ experts.

ML engineers and data scientists who know how models fail, and who can write that down as tests.

023,200+experts

ML engineers and data scientists who know how models fail. They design evaluations, write rubrics and audit training data and pipelines.

Degree coverage

  • 1,300+ with ML, AI or data science titles or degrees
  • 1,800+ in LLM evaluation, RLHF and red-teaming
  • 750+ master's, 170+ PhDs

Work we staff

Eval and benchmark designRubric writingTraining data review

Typical engagements

  • Evaluation and benchmark design with a written quality bar.
  • Rubrics that turn expert judgment into criteria a grader can check.
  • Audits of training data and model pipelines.

Counts are experts in our network whose resume shows a degree, a role or real experience in the domain, or who applied in it, so one person can count in more than one domain. Degrees count only when completed. Read from their resumes, as of September 2026; every figure is rounded down.

How We Work

From first call to production, one team.

01

Scope

You talk to the people who will build it. We agree the task spec, the quality bar and how we'll measure it.

02

Pilot

A small paid batch you can grade yourself. We fix what you flag before anything scales.

03

Scale

The same vetted experts, more of them, with review layers and regular reporting.

Trust

  • 01Signed agreementsEvery contributor signs a contributor agreement, a confidentiality agreement and a code of conduct before seeing any project material.
  • 02Identity checksGovernment ID and selfie verification for the experts on your project when your work needs it, so you know who is doing it.
  • 03Confidential by defaultProject material we host is visible only to the experts on that project, behind their own sign-in.
  • 04A person answersExperts and clients reach a named person on our team, not a ticket queue.
Next step

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