Remote Research Physics Expert - AI Trainer

Role Overview

We are seeking expert physics researchers to author and verify golden reference solutions for the Crit Pt benchmark (arXiv:v3) — a frontier research-level physics benchmark. Participants will solve Crit Pt research-level problems end-to-end, audit solutions from other experts, or adjudicate between parallel solution attempts, producing 100%-human-verified reference data used to evaluate large language models on frontier physics reasoning.

High Energy Physics & Mathematical Physics, Biophysics & Statistical Physics, Condensed Matter & AMO, Gravitation / Cosmology / Astrophysics, Quantum Information, Optical Properties of Materials, Magnetic Materials, Measurements in QM.

• Solve research-level physics challenges end-to-end with verifiable derivations, code, and peer-reviewed references
• Decompose challenges into standalone checkpoint sub-problems that require genuine physical reasoning
• Author Python answer templates with auto-grading functions for symbolic or numerical answers
• Audit submitted solutions for correctness, scope, and method soundness; deliver actionable feedback across iterations
• Adjudicate between parallel solver attempts and decide which solution becomes the golden reference
• Document chain-of-thought reasoning, error tolerances, equivalent symbolic forms, and verification test cases

• Solver:
  PhD or postdoc in the relevant subfield (senior PhD student minimum)
• Auditor:
  Postdoc or junior professor in the relevant subfield (PhD minimum)
• Adjudicator:
  Full professor or industry research PI in the relevant subfield (senior postdoc or junior professor minimum)
• Hands-on familiarity with at least two canonical methods of the target subfield, demonstrable through publications (broader coverage strongly preferred)
• 3–5 representative publications (arXiv  DOI), ideally within the last ~5 years and in the target subfield
• Working proficiency with LaTex, Python, Jupyter, and Sym Py
• Strong written English (B2/C1/C2 minimum; native or near-native preferred)

• Expected commitment: ~10 hours/week, sustained across an 8–10 week window per task pool
• Pay range: $80–$140 per hour, based on role and demonstrated expertise
• Asynchronous work