$110–250/hr · Mercor · Part time
Physician expert contributing medical judgment to AI training by evaluating clinical scenarios, diagnosing reasoning, and assessing real-world applicability.
What you would do
- Review and critique AI-generated clinical content for medical accuracy and appropriateness
- Evaluate diagnostic decision-making against real-world patient scenarios and best practices
- Assess AI recognition of edge cases, rare conditions, and atypical presentations
- Document your clinical reasoning and identify gaps in AI model understanding
- Provide feedback on patient care workflows and treatment protocols
Who they want
- MD, DO, or equivalent clinical license with active practice or recent clinical experience
- Strong knowledge of clinical diagnosis, treatment pathways, and patient management
- Ability to articulate medical reasoning clearly to non-physicians
- Comfort documenting complex clinical judgment for training data
- Familiarity with HIPAA, patient privacy, and healthcare compliance requirements
Main skills
What the interview asks about
1.Diagnostic reasoning for atypical cases
AI models trained on typical presentations miss rare conditions and unusual symptom combinations, so interviewers test whether you catch clinically important edge cases.
For example: “An AI recommends antibiotics for fever in a young healthy patient. You suspect immunosuppression or atypical infection. What features would make you question this and how would you document feedback?”
2.Compliance and privacy boundaries
Medical AI projects must navigate HIPAA and ethics rules; interviewers need to confirm you understand what patient details can inform feedback and what stays confidential.
For example: “A training scenario involves a patient with a rare comorbidity you recognize from your own practice. How do you provide accurate medical feedback without inadvertently revealing or inferring the patient's identity?”
3.Nuance in clinical uncertainty
Real medicine involves uncertainty and probabilities; interviewers test whether you can convey confidence levels and alternative diagnoses so the model learns when to express doubt.
For example: “Given a partial clinical picture, how would you evaluate an AI model that outputs a single confident diagnosis versus one that suggests multiple possibilities with probability scores? Which better mirrors how you approach diagnostic uncertainty?”
4.Communication with non-medical stakeholders
AI researchers need to understand clinical significance; interviewers assess whether you translate medical concepts into actionable insights for engineers and product teams.
For example: “You're flagging a gap in the model's handling of drug-drug interactions. Explain to a non-physician AI researcher why this matters, what real harm could occur, and how you'd describe the scenario for training data without using medical jargon.”
A task you may get
Review a synthetic case with complex presentation and multiple diagnoses. Critique the AI's reasoning, identify missed context, document feedback with confidence levels, and suggest training data improvements.
How to prepare
- Refresh your knowledge of diagnostic algorithms and differential diagnosis frameworks in your specialty.
- Review several cases where you changed your initial diagnosis; document the clinical features that prompted reconsideration.
- Familiarize yourself with how medical AI systems are evaluated (accuracy, sensitivity, specificity, edge-case coverage).
- Prepare examples of complex patient scenarios where textbook protocols needed adjustment based on individual patient factors.
The facts
- Pay
- $110–250/hr
- Hours
- Part time
- Where
- Remote
- Field
- Medicine
- Role type
- Talent network
- Posted
- 2/27/2026
We wrote this page from the public Mercor listing. It may be out of date, so read the full posting before you apply.