Remote Medical Annotator Intern for AI Healthcare Data

Globik AI; United States; À distance; USD 117 000 - 220 000; Temps partiel

Résumé du poste

Globik AI is seeking a detail-oriented Medical Annotator Intern to support AI model development in healthcare. You will label and validate medical data such as clinical notes, radiology reports, and patient records to improve model accuracy.

This remote internship requires strong analytical skills, basic medical knowledge, and the ability to work independently. You will collaborate with AI engineers and quality teams to ensure high-quality annotated datasets while maintaining thorough

Qualifications

• Must be a student or recent graduate in Medicine, Life Sciences or related fields.
• Basic understanding of medical terminology and clinical workflows.
• Familiarity with AI/ML concepts is a plus.
• Strong attention to detail and analytical skills.
• Ability to work independently in a remote setup.

Responsabilités

• Review and annotate medical datasets including clinical notes and reports
• Label entities such as symptoms, diagnoses, and treatments
• Ensure high accuracy and consistency in annotations
• Validate AI model outputs against medical standards
• Collaborate with data scientists to improve annotation guidelines
• Maintain documentation of annotation processes
• Perform quality checks on annotated datasets

Connaissances

Attention to detail

Analytical mindset

Medical knowledge (basic)

Remote work ability

Formation

Students or recent graduates in Medicine/Life Sciences or related fields

Description du poste

Globik AI is seeking a detail-oriented Medical Annotator Intern to support AI model development in healthcare. You will label and validate medical data such as clinical notes, radiology reports, and patient records to improve model accuracy.

This remote internship requires strong analytical skills, basic medical knowledge, and the ability to work independently. You will collaborate with AI engineers and quality teams to ensure high-quality annotated datasets while maintaining thorough