Remote Condensed Matter Physicist - AI Research Evaluator

AuraOne, Inc.; United States; Remote; USD 110,000 - 152,000; Full time

Benefits offered by this job

Remote - US eligible

Job summary

AuraOne, Inc. seeks a Condensed Matter Physicist (PhD) for AI Research Evaluation, reviewing physics reasoning and derivations in model outputs. You reproduce key steps and document the correct methods to train the modeling team.

This remote contractor role requires disciplined, high-precision analysis and clear written explanations. Applicants should have graduate-level physics training, publication or advising experience, and the ability to apply rubrics across long batches with async

Qualifications

• Graduate-level training or equivalent applied experience in physics or a closely related field.
• Hands-on experience publishing, teaching, or advising on the topic at a professional level.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that cites methods, papers, or worked examples.

Responsibilities

• Review AI outputs against current physics methods and standards.
• Reproduce or sanity-check key derivations, calculations, or claims.
• Flag dimensional, methodological, and citation errors with structured tags.
• Capture corrected reasoning or worked examples for training data.
• Adjudicate disputed answers using textbooks or community standards.
• Maintain reviewer-quality scores in calibration cycles.

Skills

Physics expertise

Academic publishing

Clear written reasoning

Async availability

Education

PhD in Physics

Job description

AuraOne, Inc. seeks a Condensed Matter Physicist (PhD) for AI Research Evaluation, reviewing physics reasoning and derivations in model outputs. You reproduce key steps and document the correct methods to train the modeling team.

This remote contractor role requires disciplined, high-precision analysis and clear written explanations. Applicants should have graduate-level physics training, publication or advising experience, and the ability to apply rubrics across long batches with async