Dermatology Domain Expert

OpenTrain AI, Inc.; United States; Remote; USD 21,000 - 34,000; Part time

Job summary

OpenTrain AI, Inc. seeks up to part-time dermatology experts to conduct clinical reviews of complex dermatology cases for AI evaluation. You will resolve reviewer disagreements and help shape evaluation standards. The role is remote, with occasional synchronous calls and US-based eligibility.

The position requires board-certified dermatology and a US medical license, with at least three years of post-residency clinical practice and an academic appointment at Assistant Professor level or higher.

Qualifications

• Must hold a US medical license and board certification in dermatology.
• 3+ years post-residency with active clinical practice.
• Assistant Professor level faculty appointment at a US medical school or academic medical center.
• Fluent in English.

Responsibilities

• Provide expert clinical reviews for complex dermatology cases and AI evaluation.
• Resolve disagreements among reviewers and document defensible consensus.
• Contribute to evaluation frameworks and write guideline-aligned commentary.
• Advise review teams on AI model outputs and provide subspecialty consultation.
• Follow project protocols and disclose conflicts of interest.
• Work with graphic medical images and clinically complex material.

Skills

Written clinical reasoning

Consensus leadership

Peer collaboration

Clinical complexity

Education

Assistant Professor level faculty appointment (US)

Board certification in dermatology

Job description

The work

You will provide expert clinical review for dermatology data and AI evaluation. The work focuses on complex or unclear cases, clinical reasoning, and guideline-aligned documentation.

You will also advise review teams on model outputs and help create reliable evaluation standards. The role is remote and asynchronous, with occasional synchronous calls.

• Review complex or ambiguous dermatology cases.
• Resolve disagreements among reviewers and document clinically defensible consensus.
• Contribute to clinical evaluation frameworks and write guideline-aligned commentary.
• Advise review teams on AI model outputs and provide subspecialty consultation.
• Follow project protocols and disclose relevant conflicts of interest.
• Work with graphic medical images and clinically complex material.

What it pays and takes

This is a part-time independent-contractor engagement with assignments based on project needs. The initial duration is four weeks, and payment is made per label.

• Pay: Per label.
• Workload: 20 or more hours per week.
• Location: Remote; open to candidates in the United States.
• Language: Fluent in English.
• Experience level: Entry level.
• Education: Faculty appointment at the Assistant Professor level or above at an accredited US medical school or academic medical center.
• Certification: Board certification in dermatology from the American Board of Dermatology.
• Clinical background: At least three years after residency completion, an active clinical practice, and an active medical license in good standing in at least one US state.
• Skills: Strong written clinical reasoning, consensus leadership, reliable collaboration with peer faculty reviewers, and comfort with clinical complexity.

Helpful background

The following experience is valued but is not listed as required. It can be useful for reviewing specialized dermatology cases and evaluating clinical AI systems.

• Fellowship training in dermatopathology, Mohs or procedural dermatology, pediatric dermatology, or complex medical dermatology.
• Peer-reviewed dermatology publications or journal peer review.
• IRB or research committee service.
• Teaching residents or fellows.
• Involvement with a specialty society.
• Previous clinical AI evaluation, digital health, or dermatology AI research.

About AI training work

AI training work uses human review to help build and improve artificial intelligence systems. Medical experts are important because they can assess complex cases, explain their reasoning, and judge whether model outputs are clinically sound.