Remote Research QA Specialist — Synthesis & AI Validation

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

Job summary

AuraOne is seeking a Research Synthesis Research QA Specialist for a remote contractor role to evaluate AI outputs across synthesis, QA, reasoning, calculations, and workflows. Reviewers grade derivations, reproduce key results, and document the correct method so the modeling team can train on it.

AuraOne uses scientific specialists to grade outputs, check assumptions, reproduce steps, and capture the right method alongside the wrong one.

Qualifications

• Graduate-level training or equivalent applied experience in research synthesis research qa 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.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Review AI outputs against current research synthesis research qa research review methods, conventions, and prior work for Research Synthesis Research QA 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 QA

Web research

Source grounding

Browser automation

Research

Synthesis

Education

PhD or higher in related field

Job description

AuraOne is seeking a Research Synthesis Research QA Specialist for a remote contractor role to evaluate AI outputs across synthesis, QA, reasoning, calculations, and workflows. Reviewers grade derivations, reproduce key results, and document the correct method so the modeling team can train on it.

AuraOne uses scientific specialists to grade outputs, check assumptions, reproduce steps, and capture the right method alongside the wrong one.