Senior Domain Expert — Multi-Discipline AI Research Program
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About the role
Pay: $135/hour
We're building a research cohort of senior domain experts across seven specialist tracks — software/systems engineering, formal methods and computational science, ML inference and GPU kernels, enterprise operations, security, hardware design, and creative technology — to help evaluate how frontier AI models reason about hard, real-world expert-level problems. This is research-and-evaluation work, not production engineering: you'll be defining what "correct" and "excellent" look like on problems you already know deeply.
Candidates must fall into one of the profiles listed below to be considered.
Tracks & Requirements:
- Senior Software/Systems Engineer
Domain: Software Engineering
Experience: 8–12 years
Education: Bachelor's (Master's preferred) - Research Scientist — Formal Methods / Computational Science
Domain: Science (Math / Physics / Chemistry / Biology)
Experience: 10+ years
Education: PhD/Doctorate required - ML Research Engineer — Inference & GPU Kernels
Domain: Machine Learning / AI
Experience: 6–10 years
Education: PhD/Doctorate, or Master's with a strong research record - Enterprise Operations / Domain Analyst
Domain: Operations (Supply Chain / Finance / Compliance)
Experience: 8–12 years
Education: Bachelor's (professional certifications a plus) - Security Engineer — Cryptanalysis / Reverse Engineering
Domain: Security
Experience: 6–10 years
Education: Bachelor's (Master's preferred) - Mechanical / Hardware Design Engineer
Domain: Hardware (CAD / RTL / Robotics)
Experience: 8–12 years
Education: Bachelor's (PE license or Master's a plus) - Creative Technologist — Audio / Design / Linguistics
Domain: Media
Experience: 6–10 years
Education: Bachelor's (Master's a plus)
Candidates who meet the requirements and provide the requested materials will be prioritized for review. As part of this process, we conduct thorough background checks. Please apply only if you meet these requirements — candidates who meet fewer than 70% of the stated qualifications may be flagged for misrepresenting their professional experience, which could affect eligibility for future project staffing.