Remote Chemistry AI Quality Reviewer (PhD)

AI Trainer Jobs; United States; Remote; USD 165,000 - 190,000; Part time

Job summary

AI Trainer Jobs seeks a Chemistry Professor/Researcher (PhD) to remotely review AI outputs across chemistry reasoning, calculations, and research workflows. Reviewers grade derivations, reproduce key results, and document the correct method so the modeling team can train on it.

Chemistry models live or die on whether their derivations hold up under scrutiny. This role involves applying rigorous vetting, reproducing steps, and capturing the right method alongside the wrong one for training data.

Qualifications

• Graduate-level training or equivalent applied experience in chemistry.
• 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 chemistry methods, conventions, and prior work for Chemistry Professor/Researcher (PhD) 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

Chemistry

Scientific reasoning

Method validation

Citation review

Quantitative analysis

AI model evaluation

Education

PhD in chemistry or related field

Job description

AI Trainer Jobs seeks a Chemistry Professor/Researcher (PhD) to remotely review AI outputs across chemistry reasoning, calculations, and research workflows. Reviewers grade derivations, reproduce key results, and document the correct method so the modeling team can train on it.

Chemistry models live or die on whether their derivations hold up under scrutiny. This role involves applying rigorous vetting, reproducing steps, and capturing the right method alongside the wrong one for training data.