Remote AI Research Evaluation Specialist (PhD)

AI Trainer Jobs; United States; Remote; USD 141,000 - 190,000; Part time

Job summary

AI Trainer Jobs is seeking a Research Evaluation Specialist (PhD / Researcher / Professor) for remote, contractor work to evaluate AI outputs across research evaluation workflows. The role involves grading derivations, reproducing key results, and documenting correct methods to train modeling teams.

Responsibilities include reviewing outputs, reproducing claims, flagging errors with severity tags, and documenting corrected reasoning for training datasets.

Qualifications

• Graduate-level training or equivalent applied experience in research evaluation research review or a closely related field for Research Evaluation Specialist 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 research evaluation methods, conventions, and prior work for Research Evaluation Specialist 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

Research evaluation review

AI model evaluation

Education

PhD or equivalent

Job description

AI Trainer Jobs is seeking a Research Evaluation Specialist (PhD / Researcher / Professor) for remote, contractor work to evaluate AI outputs across research evaluation workflows. The role involves grading derivations, reproducing key results, and documenting correct methods to train modeling teams.

Responsibilities include reviewing outputs, reproducing claims, flagging errors with severity tags, and documenting corrected reasoning for training datasets.