Remote Family Medicine Physician — AI Data Annotator

RemoteJobsOne; Atlanta (GA); Remote; USD 138,000 - 165,000; Part time

Job summary

RemoteJobsOne in the United States is seeking licensed Family Medicine physicians to contribute their clinical expertise to a dynamic AI training project. This fully remote contractor role is open to candidates based in the United States and offers hourly compensation.

You will annotate and review office visit notes, labs, and care plans, apply evidence-based guidelines, provide feedback, and collaborate with project stakeholders to resolve ambiguities and improve model understanding.

Qualifications

• Active MD degree and board cert in Family Medicine are required.
• Based in the United States and licensed to practice in the U.S.
• Hands-on clinical experience in family medicine settings.
• Broad clinical exposure across common acute and chronic conditions, preventive care, and multiple age groups.
• Previous annotation or clinical review experience is a plus.
• Proficiency with written and verbal communication.

Responsibilities

• Annotate and review clinical content and medical records for accuracy and completeness.
• Evaluate AI-generated outputs for clinical accuracy, safety, and appropriateness across diverse patient populations.
• Apply clinical judgment and knowledge of guidelines to validate and enrich datasets.
• Collaborate with project stakeholders to clarify requirements and resolve ambiguities in clinical documentation.
• Provide high-quality feedback to improve project workflows and model understanding.

Skills

Communication skills

Clinical judgment

Attention to detail

Education

MD degree

Board certification in Family Medicine

Job description

RemoteJobsOne in the United States is seeking licensed Family Medicine physicians to contribute their clinical expertise to a dynamic AI training project. This fully remote contractor role is open to candidates based in the United States and offers hourly compensation.

You will annotate and review office visit notes, labs, and care plans, apply evidence-based guidelines, provide feedback, and collaborate with project stakeholders to resolve ambiguities and improve model understanding.