Remote AI Research & Reasoning Reviewer

AI Trainer Jobs; United States; Remote; USD 136,000 - 194,000; Full time

Job summary

AI Trainer Jobs is seeking a remote contractor to review science, research, and technical reasoning outputs for AI research and evaluation. You will catch flawed assumptions, weak reasoning, and incorrect terminology before those errors become training signals.

Responsibilities include reproducing key derivations, flagging errors with severity tags, and documenting corrected reasoning so the modeling team can improve future evaluations.

Qualifications

• Graduate-level training or equivalent applied experience in leading university research program reviews.
• Experience publishing, teaching, or advising at a professional level.
• Ability applying 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 leading university research program us parents children ages 9 13 research review methods, conventions, and prior work for Leading University Research Program — US Parents & Children Ages 9–13 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.

Skills

Scientific reasoning

Method validation

Citation review

Quantitative analysis

Domain expertise

Education

Graduate-level training or equivalent

Job description

AI Trainer Jobs is seeking a remote contractor to review science, research, and technical reasoning outputs for AI research and evaluation. You will catch flawed assumptions, weak reasoning, and incorrect terminology before those errors become training signals.

Responsibilities include reproducing key derivations, flagging errors with severity tags, and documenting corrected reasoning so the modeling team can improve future evaluations.