Remote STEM AI Review and Validation Specialist

AuraOne; United States; On-site; USD 76,000 - 110,000; Full time

Job summary

AuraOne—a remote review track for STEM research—seeks experienced reviewers with graduate-level STEM training to evaluate AI outputs, reproduce key steps, and document correct methods for training data.

As a contractor, you will apply multi-page rubrics, cite sources, and provide clear reasoning. Flexible async hours (at least 10 per week) are expected to maintain calibration and quality across batches.

Qualifications

• Graduate-level training or equivalent applied experience in STEM research.
• 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 STEM research methods, conventions, and prior work for STEM PhDs and Technical Domain Experts 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

Method validation

Citation review

Quantitative analysis

STEM research

Education

Graduate-level STEM training

Job description

AuraOne—a remote review track for STEM research—seeks experienced reviewers with graduate-level STEM training to evaluate AI outputs, reproduce key steps, and document correct methods for training data.

As a contractor, you will apply multi-page rubrics, cite sources, and provide clear reasoning. Flexible async hours (at least 10 per week) are expected to maintain calibration and quality across batches.