hackajobis collaborating withMercorto connect them with exceptional professionals for this role.
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.
