Remote STEM Research Auditor & Derivation Reviewer

AuraOne; United States; Remote; USD 55,000 - 96,000; Full time

Job summary

AI Trainer Jobs is seeking a STEM Expert to remotely review AI outputs in stem research reasoning, calculations, and workflows. Reviewers grade derivations and reproduce key results, documenting the correct method so the modeling team can train on it.

STEM research models require rigorous evaluation and clear justification. This role emphasizes applying rubrics consistently across long batches and providing precise written reasoning for training data preparation.

Qualifications

• Graduate-level training or equivalent applied experience in stem research.
• 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 Expert 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

Education

PhD or equivalent

Job description

AI Trainer Jobs is seeking a STEM Expert to remotely review AI outputs in stem research reasoning, calculations, and workflows. Reviewers grade derivations and reproduce key results, documenting the correct method so the modeling team can train on it.

STEM research models require rigorous evaluation and clear justification. This role emphasizes applying rubrics consistently across long batches and providing precise written reasoning for training data preparation.