Role brief
Job description
## About the Role
An AI healthcare company that automates administrative and pharmacy workflows to help patients access life-changing medications and specialty treatments is seeking an experienced Cardiologist for a part-time consulting role. The role focuses on improving an AI model that identifies patients likely to have **transthyretin (ATTR) cardiac amyloidosis** through clinical chart review and evidence-based reasoning.
## What You’ll Do
– Review patient charts and assess the likelihood of ATTR cardiac amyloidosis, including cases with incomplete information.
– Document clinical reasoning using specific, evidence-based criteria; distinguish ATTR from similar conditions and communicate uncertainty.
– Apply consistent labeling standards and refine assessments based on new evidence and AI model feedback.
– Explain clinical decisions clearly to engineers and data scientists and complete labeling batches within agreed timelines.
## Who Should Apply
– MD or DO with completed Internal Medicine residency and Cardiology fellowship.
– Currently practicing Cardiologist with **2+ years of post-fellowship experience**.
– Hands-on experience working up and managing patients with suspected or confirmed **ATTR cardiomyopathy**, including ordering and interpreting PYP scans.
– Strong preference for Heart Failure or Transplant Cardiologists with advanced heart failure experience, or specialists in amyloidosis or cardiomyopathies.
– Able to commit **10–20 hours per week** and translate complex clinical judgments into clear, structured criteria.
**Preferred:** Experience at amyloidosis center of excellence or cardiomyopathy clinic; publications on ATTR cardiomyopathy screening, epidemiology, or diagnostic pathways; or previous work with AI/ML teams, clinical decision-support systems, or clinical chart labeling.
## Project Details
– **Engagement:** Part-time consulting with a commitment of 10–20 hours per week.
– **Required Role:** US-based clinicians only.
– **Focus:** Patient chart annotation, clinical reasoning, and AI model improvement.