Remote AI Evaluation Physicist — Fluid Dynamics & Convection

AuraOne; United States; Remote; USD 110,000 - 152,000; Part time

Job summary

AuraOne is seeking a Fluid Dynamics and Convection Stability Physicist (PhD) for AI Evaluation. This remote review track focuses on evaluating physics reasoning, calculations, and research workflows, grading derivations and reproducing key steps.

Responsibilities include reviewing outputs against physics methods, reproducing key results, flagging errors with severity tags, and capturing corrected reasoning to train the model team.

Qualifications

• Graduate-level training or equivalent in physics for Fluid Dynamics and Convection Stability.
• Experience publishing, teaching, or advising on the topic at a professional level.
• Ability to apply multi-page rubrics consistently across long batches.
• Clear written reasoning citing methods, papers, or worked examples.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Review AI outputs against current physics methods, conventions, and prior work for Fluid Dynamics and Convection Stability Physicist (PhD) — AI 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.

Skills

Scientific reasoning

Method validation

Citation review

Quantitative analysis

Physics

Education

PhD in Physics

Job description

AuraOne is seeking a Fluid Dynamics and Convection Stability Physicist (PhD) for AI Evaluation. This remote review track focuses on evaluating physics reasoning, calculations, and research workflows, grading derivations and reproducing key steps.

Responsibilities include reviewing outputs against physics methods, reproducing key results, flagging errors with severity tags, and capturing corrected reasoning to train the model team.