Senior Domain Expert — Multi-Discipline AI Research Program

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.