Employer description & source details
Review annotations produced by human annotators, clinical experts, external providers, and AI/ML models to ensure accuracy and consistency. Identify, document, and escalate ambiguous cases, annotation discrepancies, and data-quality risks for clinical review or adjudication. Participate in annotation training, calibration, qualification, and inter-annotator agreement activities. Maintain annotation guidelines, quality criteria, review decisions, dataset changes, and other supporting documentation. Support traceability between source data, annotation versions, quality reviews, adjudication decisions, and approved dataset outputs. Collaborate with engineers, researchers, surgeons, clinical experts, and external providers to translate clinical data requirements into annotation and review activities. Contribute to clinical data collection, tool and model evaluation, and workflow improvements that increase efficiency while maintaining clinical accuracy and data quality. Conduct clinical research through literature review, procedural analysis, and consultation with clinical experts to support AI/ML research and product-development needs. Bachelor’s degree in biology, physiology, biomedical engineering, nursing, clinical sciences, health sciences, or a related field. Strong understanding of human anatomy and an interest in learning surgical procedures across multiple specialties. Experience interpreting medical images, surgical videos, clinical workflows, or other complex visual data. Experience with data annotation, clinical data review, medical research, clinical operations, or a related field. Ability to learn and use specialized annotation platforms, data-management systems, and project-tracking tools. Strong analytical and problem-solving skills, including the ability to
Extracted skills: machine learning, artificial intelligence, operations