Own the full lifecycle of clinical text extraction models, from problem framing and annotation strategy through production deployment, evaluation, monitoring, and retraining. Build clinical NLP pipelines, establish defensible quality and error-analysis systems, and apply LLMs using rigorous evaluation, observability, and cost controls. Partner with clinical data and backend engineering teams while addressing PHI protection, de-identification, auditability, and access controls. As the first ML hire, set technical direction and potentially build and lead the future ML team.

Build scalable backend services and data pipelines to process large healthcare datasets, integrate and optimize LLM-driven features, implement observability and best practices, collaborate across frontend, data, and AI teams, and own end-to-end feature delivery for production systems.