Family Medicine Physician for AI Model Training

Use your family medicine or internal medicine clinical expertise to review, annotate, and validate real-world clinical data that will improve the safety, accuracy, and explainability of medical AI systems. This is an in-person position based in San Francisco and part of an ongoing talent network for repeated project staffing.

• Clinical data annotation: Review and label clinical text, EHR records, and case notes for AI model training. Identify and validate medical entities, diagnoses, treatment pathways, and outcomes relevant to family medicine.
• Quality review and validation: Audit annotated datasets for clinical accuracy and consistency. Cross-check AI-generated outputs for medical soundness.
• Knowledge contribution: Provide expert input on annotation guidelines, taxonomy development, and definitions for edge cases. Collaborate with data scientists and engineers to improve AI interpretation of clinical context.
• Model evaluation and feedback: Evaluate AI-generated recommendations or clinical summaries, flag inaccuracies, and deliver structured feedback for iterative model refinement.
• Documentation and training support: Help create clinical documentation standards and assist in developing onboarding materials for new annotators.

• MD or DO degree with specialization in Family Medicine or Internal Medicine.
• Board-certified or board-eligible in Family Medicine or Internal Medicine.
• At least 2+ years of clinical experience in in-patient or hospitalist care settings.
• Academic hospital experience preferred.

• Location: On-site, San Francisco, California.
• Employment type: Hourly engagement, in person.
• Ongoing hiring cadence: multiple experts are hired every few weeks as projects open.

• Hourly rate: 170 - 190 hourly.

• Must hold MD or DO degree and be board-certified or board-eligible in Family Medicine or Internal Medicine.
• Minimum of 2+ years clinical experience in in-patient or hospitalist settings.

This role is filled via a talent network. Submit your details to join the network; your information will be stored for consideration for multiple upcoming projects. Candidates from the network are reviewed on an ongoing basis and selected for specific engagements as opportunities become available, with new hires added every few weeks.