Senior Domain Expert - AI Research Evaluation

What You'll Do5

• Build realistic technical scenarios and problem sets that reflect the work of your track.
• Author reference solutions at expert level and scoring rubrics that separate correct work from weak work.
• Judge AI-produced answers on accuracy, technical depth, and the quality of domain reasoning.
• Apply the standards of your field when weighing ambiguous or borderline answers.
• Record examples of strong and flawed reasoning so others can calibrate the evaluation.

Requirements11

• Meet the experience and education threshold for at least one track in the program.
• Remain active in the field through hands-on work, not only academic or advisory roles.
• Write clear technical explanations and structured feedback in English.
• Handle independent work across remote and asynchronous schedules.
• Show published research, open-source contributions, or recognized work product in your domain (preferred).
• Bring prior experience grading, reviewing, or evaluating other people's technical work (preferred).
• For ML work, highlight SGLang, vLLM, Mamba/Mamba2, TensorRT-LLM, or GPU kernel projects (bonus).
• For science work, highlight Lean/Coq, computational biology or genomics, or condensed-matter and quantum physics (bonus).
• For security work, highlight reverse engineering, cryptanalysis, or CTF experience (bonus).
• For hardware work, highlight RTL/HDL, CAD, or robotics experience (bonus).
• For media work, highlight audio engineering, linguistics, or multimodal design (bonus).

Who Should Apply

A senior practitioner in one of the seven tracks who still does the work and can explain it in writing fits this role. You have reviewed or graded technical work before, and you can point to publications, open-source projects, or shipped results. Apply if you clear at least 70% of the qualifications for a specific track and can show recent hands-on depth.

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