Physics Problem Evaluator

You'll review AI-generated solutions to university-level and graduate physics problems — mechanics, electromagnetism, thermodynamics, quantum mechanics, and beyond. Each task involves reading a model's step-by-step response, identifying errors in reasoning or calculation, and rating the output on correctness, logical coherence, and depth of explanation. You won't just flag wrong answers; you'll articulate precisely where the model's reasoning broke down and, where asked, provide a corrected reference solution.

Your work spans three main workflows: ambiguous-case judgment (deciding when a non-standard approach is still valid), written technical feedback (annotating specific failure modes in a model's derivation), and problem-solving depth review (assessing whether a solution demonstrates genuine physical intuition or is just symbol-shuffling). Tasks arrive in batches via a structured web interface; you log in, claim a batch, complete it, and submit — no scheduling calls, no meetings.

The role requires at minimum a graduate-level background in physics or a closely related field. Professor- or PI-level experience is strongly preferred because many tasks involve research-adjacent material: statistical mechanics, condensed matter, or computational physics at the level of a first-year PhD course. You'll be compensated at the Expert tier and are expected to maintain accuracy above the project's threshold, which will be shared during onboarding.