Member of Technical Staff, Frontier AI

The work

Connect research, data and deployed AI through rigorous evaluations and hands on technical ownership. Work with researchers, domain experts and operators to turn dependable evidence into measurable model and system improvements.

• Lead research and evaluation from problem definition through data design, quality calibration and validation of results.
• Design ML focused data systems covering tasks, annotation schemas, rubrics, incentives and pipelines that support model performance.
• Investigate failures to identify root causes, unusual cases and opportunities for improvement.
• Convert ambiguous system behavior into structured evaluations and new data categories.
• Calibrate quality early with researchers and experts and keep improving evidential standards.
• Iterate on datasets, evaluations and feedback processes to improve system outcomes.
• Challenge unsupported claims, pause work or change scope when data quality or evidential strength is insufficient.
• Communicate research progress with client facing and other colleagues using credible, evidence supported explanations.
• Find gaps in coverage and recommend where further investment, revision or stopping is justified.

Experience and skills

• Strong judgment about research evidence and whether results are ready to communicate externally.
• Experience designing ML datasets, evaluation methods and quality assurance processes.
• Ability to organize messy operational behavior into useful research and evaluation questions.
• Decisive ownership in ambiguous situations.
• Clear communication of limitations, tradeoffs and strength of evidence for technical and general stakeholders.
• Experience working directly with experts during project launch, calibration and iteration.
• A system wide focus on improving complete model or agent performance.
• RL environments, simulators and feedback based training systems are preferred experience.
• Agent improvement, applied research or production work affecting deployed systems is valuable.
• Evaluation of complex practical tasks and expert incentive design in consequential projects are helpful.

Working arrangements

This full time remote position has a national base salary range of $180,000 to $320,000 per year. Employees are eligible for equity, and performance bonuses may apply depending on role and policy.

Benefits include reimbursement of up to 100% of health insurance premiums, paid time off and a 401(k) with an employer match.