Senior Dermatology AI Evaluation Reviewer

You will review complex or ambiguous dermatology cases and assess AI model outputs using structured clinical judgment. The work includes graphic medical images, clinical data curation, and guideline-aligned documentation.

You will help resolve disagreements between reviewers, document defensible reasoning, and contribute expert input to clinical evaluation frameworks. You may also provide subspecialty consultation across assigned case batches.

• Lead senior-level review of complex or ambiguous dermatology cases.
• Review AI model outputs and provide expert clinical guidance.
• Resolve reviewer disagreements through a structured consensus process.
• Write guideline-aligned comments and document clinical reasoning.
• Work with clinical complexity and graphic medical images.

This is a remote, asynchronous independent contractor engagement with occasional synchronous calls. The engagement is expected to last four weeks.

• Pay:
  Per task, with the amount based on experience, academic standing, and clinical expertise.
• Schedule:
  
  20 or more hours per week.
• Location:
  
  Open to candidates in the United States.
• Language:
  Professional fluency in English.
• Experience level:
  Entry level.
• Education and appointment: A 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 of experience after residency completion, an active medical license in good standing in at least one US state, and an active clinical practice.
• Required skills:
  
  Strong written clinical reasoning, the ability to lead and document clinical consensus, and careful adherence to project protocols.
• Helpful background:
  Fellowship training in dermatopathology, Mohs or procedural dermatology, pediatric dermatology, or complex medical dermatology.
• Also relevant:
  Peer-reviewed dermatology publications, journal peer review, IRB or research committee service, resident or fellow teaching, specialty society involvement, or prior clinical AI, digital health, or dermatology AI research.
• Disclosure:
  Be prepared to disclose applicable conflicts of interest involving dermatology AI, digital health, or pharmaceutical organizations.

AI training is the human work behind artificial intelligence, including reviewing examples and rating model outputs. Experienced clinical reviewers help make these evaluations accurate, consistent, and useful for improving AI systems.