Family Medicine Physician for AI Model Training
SaidGig
Role Overview
Use your family medicine or internal medicine clinical expertise to review, annotate, and validate clinical records and EHR-derived data for the development of safe, accurate, and explainable medical AI decision-support tools. This is an in-person role based in San Francisco, with multiple clinicians hired on a recurring basis every few weeks.
Key Responsibilities- Clinical data annotation, including review and labeling of clinical text, EHR data, and case notes for AI model training, identifying medical entities, diagnoses, treatment pathways, and outcomes relevant to family medicine.
- Quality review and validation of annotated datasets, auditing for clinical accuracy and consistency, and cross-checking AI outputs for medical soundness.
- Provide expert input on annotation guidelines, taxonomy development, and edge case definitions to improve clinical coverage.
- Evaluate AI-generated recommendations and clinical summaries, flag inaccuracies, and deliver structured feedback to support iterative model improvement.
- Contribute to clinical documentation standards and help develop onboarding and training 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 is preferred.
- Location, on-site in San Francisco.
- Employment type, hourly engagement.
- Multiple openings are filled regularly, candidates accepted into the talent network will be considered for upcoming projects as they become available.
- Hourly rate, 170 to 190 per hour.
- Required, MD or DO degree and board-certified or board-eligible status in Family Medicine or Internal Medicine.
- Minimum 2 years clinical experience in in-patient or hospitalist settings.
Join the talent network by submitting your information to be considered for this role and future related engagements. Candidates added to the network are kept on file and may be contacted for projects that open periodically. Placement for specific assignments depends on project availability and hiring cycles.
Vacancy posted more than 2 months ago
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