Remote Condensed Matter Physicist | AI Research Evaluator

AuraOne; United States; À distance; USD 229 233 000 - 315 195 000; Plein temps

Résumé du poste

AuraOne is seeking a Condensed Matter Physicist with a PhD for remote AI research evaluation. You will review physics outputs, reproduce derivations, and document methods to train modeling teams. Strong analytical and written reasoning skills are essential.

Remote — US-eligible contractor role requiring at least 10 hours per week; expected work involves grading derivations, checking consistency with literature, and providing structured feedback for model improvements.

Qualifications

• Graduate-level training or equivalent applied experience in physics or a closely related field for Condensed Matter Physicist (PhD) - AI Research Evaluation work.
• Hands-on experience publishing, teaching, or advising on the topic at a professional level.

Responsabilités

• Review AI outputs against current physics methods, conventions, and prior work for Condensed Matter Physicist (PhD) - AI Research Evaluation assignments.
• Reproduce or sanity-check key derivations, calculations, or experimental claims.
• Flag dimensional, methodological, and citation errors with structured severity tags.
• Capture the corrected reasoning or worked example so the modeling team can train on it.
• Adjudicate disputed answers against textbooks, papers, or community standards.
• Maintain reviewer-quality scores in inter-rater calibration cycles.

Connaissances

Scientific reasoning

Method validation

Citation review

Quantitative analysis

Physics

Formation

PhD in physics or related field

Description du poste

AuraOne is seeking a Condensed Matter Physicist with a PhD for remote AI research evaluation. You will review physics outputs, reproduce derivations, and document methods to train modeling teams. Strong analytical and written reasoning skills are essential.

Remote — US-eligible contractor role requiring at least 10 hours per week; expected work involves grading derivations, checking consistency with literature, and providing structured feedback for model improvements.