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.

Responsibilities - Design realistic technical scenarios and problem sets within your track - Author expert-level reference solutions and grading rubrics - Evaluate AI-generated outputs for correctness, depth, and domain judgment

Required qualifications - Meets the experience and education bar for at least one track in Full Description - Currently or recently active in the field — hands-on, not purely academic-adjacent - Strong written communication — you'll be authoring technical explanations and structured feedback, not just doing the work itself - Comfortable with independent, asynchronous, remote work

Preferred qualifications - Prior research publication record, open-source contributions, or recognized work product in your track's domain - Experience evaluating, reviewing, or grading others' technical work (peer review, code review, grading, editing) - Track-specific bonus signal ML: SGLang, vLLM, Mamba/Mamba2, TensorRT-LLM, or GPU kernel work - Track-specific bonus signal Science: formal methods/theorem proving (Lean/Coq), computational biology/genomics, condensed-matter or quantum physics - Track-specific bonus signal Security: reverse engineering, cryptanalysis, CTF experience - Track-specific bonus signal Hardware: RTL/HDL, CAD, robotics - Track-specific bonus signal Media: audio engineering, linguistics, multimodal design

Why apply - Work directly on frontier AI research problems in your area of deep expertise - Fully remote, flexible, asynchronous — fits around existing work - Compensation scaled to track and seniority (see below)