Clinical Data Labeling (Emergency & Acute Care Experience) - AI Training

Job Description Role Overview We are partnering with a leading research-focused AI organization developing advanced clinical intelligence systems. We’re seeking licensed medical professionals with hands-on experience in Emergency Room, Urgent Care, or Acute Care settings to contribute their expertise in evaluating, annotating, and refining medical data for AI development. This role is ideal for clinicians who want to leverage their real-world patient care experience to shape next-generation healthcare AI—improving how technology understands acute presentations, triage reasoning, and treatment documentation. Key Responsibilities Review and label clinical cases, documentation, and AI-generated outputs for accuracy, clinical reasoning, and contextual appropriateness.Validate and refine AI-generated content related to acute care workflows, emergency triage, and differential diagnosis.Develop and assess case-based scenarios reflecting realistic emergency medicine and urgent care encounters.Collaborate cross-functionally with data scientists and clinicians to improve annotation guidelines, ensure consistency, and enhance model reliability.Provide expert feedback on alignment with clinical best practices, safety standards, and diagnostic logic. Ideal Qualifications Licensed prescriber (active and unrestricted license in the U.S.) as one of the following:Nurse Practitioner (NP)Physician (MD or DO)Clinical Psychologist (PhD or PsyD, with prescriptive authority)PsychiatristAt least 1 year of post-licensure clinical experience in Urgent Care, Emergency Room, or Acute Care.Demonstrated ability to interpret, document, and evaluate acute clinical scenarios and medical decision-making.Familiarity with electronic medical record (EMR) workflows and documentation standards (e.g., Epic, Cerner, Meditech).Exceptional attention to detail and ability to identify inaccuracies or inconsistencies in complex clinical data.Strong written communication and documentation skills. Role Highlights Flexible workload: 10–20 hours per week, with potential for up to 40 hours.Fully remote and asynchronous — contribute on your own schedule.Engage directly with cutting-edge AI research shaping the future of clinical decision support and emergency medicine documentation.