Remote Clinical-Trial Evidence Reviewer

AI Trainer Jobs; United States; Remote; USD 41,000 - 83,000; Full time

Job summary

AuraOne is seeking a Clinical-Trial Evidence Reviewer for a remote contractor role to evaluate AI outputs that touch clinical review. Reviewers assess differential diagnosis reasoning, dosing logic, and guideline adherence, flag patient-safety issues, and document corrected clinical reasoning for model improvement.

You will review clinical and biomedical AI outputs, rely on credentialed clinical judgment, and apply structured rubrics to ensure safety and accuracy across long batches.

Qualifications

• Active or recent license / board certification in clinical review or an adjacent specialty for Clinical-Trial Evidence Reviewer work.
• Hands-on clinical experience reading patient cases and writing structured assessments.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that cites guidelines, evidence, or standard of care.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Review AI outputs against current clinical review guidelines and standard of care for Clinical-Trial Evidence Reviewer assignments.
• Grade differential reasoning, dosing, and red-flag handling on a structured rubric.
• Flag patient-safety issues with severity tags that match clinical-incident taxonomies.
• Capture the corrected clinical reasoning so the modeling team can train on it.

Skills

Clinical reasoning

Guideline review

Patient-safety assessment

Structured clinical writing

Clinical review

Medicine and healthcare

AI evaluation

Rubric writing

Expert review

Education

Active or recent license / board certification in clinical review

Job description

AuraOne is seeking a Clinical-Trial Evidence Reviewer for a remote contractor role to evaluate AI outputs that touch clinical review. Reviewers assess differential diagnosis reasoning, dosing logic, and guideline adherence, flag patient-safety issues, and document corrected clinical reasoning for model improvement.

You will review clinical and biomedical AI outputs, rely on credentialed clinical judgment, and apply structured rubrics to ensure safety and accuracy across long batches.