Senior Domain Expert — Multi-Discipline AI Research Program

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.