Remote STEM Research Auditor for AI Derivations

AI Trainer Jobs; United States; Remote; USD 170,000 - 243,000; Part time

Job summary

AuraOne is seeking a STEM Professor to remotely review AI outputs across stem research reasoning, calculations, and workflows. You will grade derivations, reproduce key results, and document the correct method so the modeling team can train on it.

Responsibilities include reproducing derivations, adjudicating disputed answers, and flagging errors with structured severity tags, while ensuring reasoning cites methods and literature for transparency and accuracy.

Qualifications

• Graduate-level training or equivalent applied experience in stem research or a closely related field for STEM Professor work.
• 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.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Review AI outputs against current stem research methods, conventions, and prior work for STEM Professor 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.

Skills

Scientific reasoning

Method validation

Citation review

Quantitative analysis

STEM research

AI model evaluation

Education

PhD or equivalent research experience

Job description

AuraOne is seeking a STEM Professor to remotely review AI outputs across stem research reasoning, calculations, and workflows. You will grade derivations, reproduce key results, and document the correct method so the modeling team can train on it.

Responsibilities include reproducing derivations, adjudicating disputed answers, and flagging errors with structured severity tags, while ensuring reasoning cites methods and literature for transparency and accuracy.