ML Engineer — Healthcare NLP

About this role

Healthcare operations teams spend enormous amounts of time reading, classifying, and extracting information from unstructured documents — provider enrollment applications, compliance correspondence, regulatory filings, and audit requests. As an ML Engineer focused on Healthcare NLP, you'll build models that automate this work, turning unstructured text into structured case data that flows through our platform. You'll work in a domain where accuracy matters enormously and where your models directly reduce the manual burden on teams processing thousands of cases.

What you'll do

• Develop and fine-tune NLP models for healthcare document classification, entity extraction, and summarization
• Build data pipelines for processing, annotating, and versioning healthcare document datasets
• Design evaluation frameworks that measure model performance against the accuracy requirements of regulated healthcare workflows
• Collaborate with domain experts to understand the structure and language of federal healthcare documents
• Deploy models into production with appropriate monitoring, versioning, and rollback capabilities
• Research and prototype new approaches for handling the unique challenges of healthcare regulatory text

What we're looking for

• 3+ years of experience building and deploying NLP models in production environments
• Strong proficiency with Python and ML frameworks (PyTorch, Hugging Face Transformers)
• Experience with named entity recognition, text classification, and information extraction tasks
• Understanding of LLM fine-tuning, prompt engineering, and retrieval-augmented generation patterns
• Experience building ML data pipelines and working with annotation workflows
• Healthcare or government domain experience is a strong plus but not required