Dermatologist - Medical AI Content Reviewer (USA)
Listing checked September 23, 2026 · pay as published by Turing
Overview
Turing, headquartered in San Francisco, builds training data and evaluation pipelines for frontier AI labs and enterprise clients. This contract brings licensed dermatologists into that pipeline as expert reviewers of model output in the medical domain. You will work inside a web-based tool, examining dermatology images for diagnostic usability and flagging problems like blur, poor lighting, exposure errors, colour shifts, and bad framing. Where the image supports it, you name the likely skin condition, give a confidence rating, and write out your reasoning. Text outputs get the same scrutiny, from diagnoses and treatment plans to case summaries. The engagement runs 12 weeks at 40 hours per week, fully remote, with at least 4 hours overlapping Pacific Standard Time each day.
What You'll Do8
- 1Assess AI-generated medical text for dermatologic accuracy, potential for harm, and completeness.
- 2Review dermatology images in a structured annotation platform and judge whether each one is usable for diagnosis.
- 3Flag defects such as blur, underexposure, colour inaccuracy, and framing problems that limit clinical value.
- 4Name probable skin conditions when the image allows it, attach a confidence score, and record the clinical rationale behind it.
- 5Check model-produced diagnoses, treatment recommendations, and case summaries for dermatologic soundness.
- 6Document your expert findings in structured questionnaires and detailed written comments.
- 7Share feedback with AI researchers and other clinicians to raise the medical integrity of model responses.
- 8Compare model output against evidence-based dermatology standards during review cycles.
Requirements8
- 1Resident in the United States and fully licensed to practice medicine there.
- 2MD or MBBS with a specialization in Dermatology or an equivalent qualification.
- 3At least 5 years of post-residency dermatology practice.
- 4Comfort working with sensitive or graphic medical imagery.
- 5Keen visual eye for image quality and diagnostic detail.
- 6Able to operate web-based annotation tools, use image zoom and markup features, and follow detailed review guidelines.
- 7Track record completing structured evaluation questionnaires with accuracy.
- 8Prior exposure to medical content review, clinical research, data annotation, or AI/ML evaluation is a plus.
Who Should Apply
This fits a US-licensed dermatologist with five or more years past residency who wants to apply clinical judgment to AI systems rather than see patients. Candidates who have reviewed medical content, contributed to clinical research, or worked on annotation and model evaluation projects tend to settle in fastest, because the work rewards precision inside a structured tool. It is a poor match for anyone outside the US, for doctors without a dermatology specialization, and for those with fewer than five years of post-residency practice, since those are hard eligibility lines. Reviewers commonly lose points by leaving rationale thin when they assign a confidence rating, or by naming a skin condition without tying it to specific visual evidence in the image. Another frequent miss is underestimating the tooling demand: navigating annotation interfaces and following long guideline documents is a real part of the job, not a formality.
Pay and demand for AI Training roles
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$70/hour
This role
Pay not disclosed
Most AI Training roles pay $38–$95 per hour.
Based on 951 similar roles that publish pay · 300 publish only a top rate; those count at the rate they gave
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Most requested skills · share of roles
- ai training15%
- ai evaluation13%
- llm evaluation12%
- data annotation10%
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Required Skills
Application Tip
Lead with your US medical license number and state, your dermatology specialization, and a clear count of post-residency years, since all three are mandatory screens. Then add any annotation, clinical research, or AI evaluation work you have done and name the platforms you used. When describing past review work, give specifics such as the volume of cases you assessed and the conditions or image types you handled, because vague claims about clinical expertise do not move a candidate past shortlisting.
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