Academic Dermatology AI Evaluation Expert

You will provide senior clinical review for a longitudinal dermatology dataset used in clinical AI development. The work is remote, asynchronous, and includes occasional synchronous calls.

You will help create defensible clinical guidance and evaluation methods for reviewing AI outputs. You may also provide dermatology subspecialty input and document reviewer consensus.

• Lead review of complex or ambiguous dermatology cases.
• Help resolve clinical disagreements among reviewers.
• Contribute to frameworks for evaluating AI models.
• Assess AI-generated clinical outputs and provide expert advice.
• Write guideline-aligned commentary that explains clinical reasoning.
• Provide relevant dermatology subspecialty consultation and document consensus.

Compensation is paid per task and determined by experience, academic standing, and clinical expertise. The engagement is expected to last four weeks.

This role requires senior academic and clinical dermatology experience, along with strong written reasoning and comfort reviewing graphic medical images and structured clinical protocols.

• Open to candidates in the United States.
• About 20 or more hours per week.
• Assistant Professor rank or above at an accredited US medical school or academic medical center.
• Board certification in dermatology by the American Board of Dermatology.
• At least three years after completing residency.
• Active clinical practice and an active medical license in good standing in at least one US state.
• Strong written articulation of clinical reasoning and familiarity with current dermatology guidelines.
• Ability to work with clinical complexity, graphic medical images, structured protocols, and reviewer disagreement.
• Helpful: fellowship training in dermatopathology, Mohs or procedural dermatology, pediatric dermatology, or complex medical dermatology.
• Helpful: peer-reviewed publications, journal peer review, IRB or research committee service, resident or fellow teaching, specialty-society involvement, or prior clinical AI evaluation and digital health research.
• Be prepared to disclose relevant conflicts involving products or organizations represented in case content.

Apply on OpenTrain with your resume, then complete the application on the hiring site.

AI training is the human work behind systems that learn from examples. In this role, clinical experts review cases and model outputs so AI systems can be assessed against accurate, guideline-based medical judgment.

Specialists are paid for this work because their subject knowledge helps make evaluations precise and useful in complex fields such as dermatology.