Internal Medicine Physician – Clinical AI Training & Evaluation
About the Role
A healthcare-focused AI research initiative is seeking internal medicine professionals to contribute to the development of advanced clinical reasoning systems. The work centers on improving how machine learning models interpret, reason through, and generate evidence-based medical guidance.
This opportunity is designed for clinically trained professionals who can apply real-world diagnostic reasoning and treatment expertise to structured evaluation and scenario design tasks. Individuals with experience in hospital medicine, outpatient internal medicine, or subspecialty practice will be particularly well aligned.
The work involves building clinically realistic case scenarios, producing expert-level reference responses, and evaluating AI-generated medical outputs against evidence-based standards where precision, safety reasoning, and guideline adherence are essential.
What You'll Do
- Design realistic internal medicine clinical scenarios including diagnosis, treatment planning, and risk assessment
- Develop expert-level “gold standard” responses based on evidence-based medical guidelines
- Evaluate AI-generated clinical outputs using structured grading rubrics
- Provide detailed written feedback to improve model clinical reasoning and safety alignment
- Contribute to scenario calibration and quality review sessions with research teams
- Ensure consistency with established clinical standards and best practices in internal medicine
- Translate real-world clinical reasoning into structured, machine-readable evaluation formats
Requirements
- Board-certified Internal Medicine physician or board-eligible clinician (residents in final year or fellows eligible)
- Active and unrestricted medical license (United States or Canada preferred)
- Strong clinical reasoning skills in diagnosis, management, and risk stratification
- Experience applying evidence-based guidelines in real patient care settings
- Ability to write clear, structured, and well-reasoned clinical explanations
- Strong attention to detail and consistency in evaluative judgment
- Comfort working independently in remote, asynchronous environments
- Ability to interpret and apply structured evaluation rubrics accurately
- Preferred: Subspecialty experience (e.g., cardiology, nephrology, endocrinology, hematology/oncology, gastroenterology, pulmonology)
- Preferred: Prior involvement in teaching, medical writing, quality assurance, or clinical research
- Preferred: Experience contributing to AI, clinical decision support, or data-driven healthcare initiatives