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
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.