Academic Reviewer Jobs (NOW HIRING)

About Turing:

Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality data, evaluations, and reinforcement learning environments that improve model capabilities in coding, reasoning, tool use, and multimodality. In coding, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across Financial Services, Life Sciences, Healthcare, Retail, Automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models.

Role Overview:

We are hiring US-based academic dermatologists to contribute senior clinical expertise to data curation for frontier AI models and labs. The work supports a longitudinal dermatology dataset used by AI developers, regulatory teams, and academic groups. This is the senior reviewer track of the project. You will lead clinical review on complex cases, contribute to the development of clinical evaluation frameworks, and serve as expert advisor for client teams reviewing AI model outputs.

The role is designed for dermatology faculty who want their academic expertise to count toward something rigorous, and who are interested in AI evaluation, methodology, and the durable infrastructure of clinical AI.

What You'll Do Day-to-Day:

• Lead senior-level clinical review on complex or ambiguous cases.
• Facilitate structured resolution of clinical disagreement among reviewers.
• Contribute to the development of clinical evaluation frameworks.
• Provide expert advisory hours to client teams reviewing AI model outputs
• Author guideline-aligned clinical commentary on cases as needed.
• Provide subspecialty consultation as appropriate

Requirements:

• Faculty appointment at the rank of Assistant Professor or above at an accredited US medical school or academic medical center
• Minimum 3 years post-residency completion
• Board-certified in dermatology by the American Board of Dermatology
• Active medical license in good standing in at least one US state
• Active clinical practice
• Subspecialty fellowship training (dermatopathology, Mohs/procedural, pediatric, or complex medical dermatology).
• Publication record in peer-reviewed dermatology journals.
• Prior exposure to clinical AI evaluation, digital health, or dermatology AI research (desired).
• Active involvement with AAD, ASDS, or other dermatology specialty societies.
• Prior journal peer review experience. Prior IRB or research committee service.
• Experience teaching residents or fellows.
• Deep familiarity with current dermatology guidelines.
• Strong written articulation of clinical reasoning suitable for peer review
• Ability to lead and document clinical consensus
• Comfort with graphic medical images and clinical complexity
• Disciplined adherence to project protocols

Performance Expectations:

• Senior reviewer responsibility on assigned case batches.
• Clinically defensible documentation.
• Consistent reliability with peer faculty reviewers
• Constructive client-facing communication during advisory hours.
• Conflict of interest disclosure.
• Applicants will be asked to disclose any active equity, advisory, consulting, or paid relationships with dermatology AI companies, digital health platforms, or pharmaceutical companies whose products may appear in case content.
• Disclosed conflicts do not automatically disqualify applicants but will be considered in case assignment.

Perks of Freelancing with Turing:

• Competitive pay and flexible remote work.
• Learn how advanced AI systems are trained.
• Be part of cutting-edge projects with top LLM companies.
• Potential for contract extension.
• Freelance perks: flexibility, global collaboration, and future-proof skills.

Offer Details:

• Independent contractor (1099): Compensation determined based on years of experience, academic standing, and clinical expertise. Anticipated average 15 hours per week; up to 40 hours per week during peak batches.
• Engagement type: Pay Per Task.
• Project and assignment-dependent timeline.
• NDA required upon engagement; detailed scope of work shared after NDA execution.
• Fully remote and asynchronous, with occasional synchronous calls.
• Duration: 4 weeks