Remote MTS: Model Training Research Reviewer

AuraOne, Inc.; United States; Remote; USD 83,000 - 138,000; Part time

Job summary

AuraOne, Inc. seeks a Member of Technical Staff, Model Training (post-training research) as a remote contractor to evaluate AI outputs through rigorous derivations, calculations, and documented methods.

The role emphasizes reproducibility and methodological accuracy across batches. The ideal candidate has graduate-level training or equivalent experience, with published or professional experience, and the ability to work asynchronously for at least 10 hours weekly while citing relevant papers and

Qualifications

• Graduate-level training or equivalent applied experience in member of technical staff model training research review or a closely related field.
• 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.

Responsibilities

• Review AI outputs against current member of technical staff model training research review methods, conventions, and prior work.
• 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.
• Maintain reviewer-quality scores in inter-rater calibration cycles.

Skills

Scientific reasoning

Quantitative analysis

Method validation

AI model evaluation

Clear written reasoning

Multilingual fluency

Education

Graduate-level training or equivalent

Job description

AuraOne, Inc. seeks a Member of Technical Staff, Model Training (post-training research) as a remote contractor to evaluate AI outputs through rigorous derivations, calculations, and documented methods.

The role emphasizes reproducibility and methodological accuracy across batches. The ideal candidate has graduate-level training or equivalent experience, with published or professional experience, and the ability to work asynchronously for at least 10 hours weekly while citing relevant papers and