Senior Applied ML Engineer

Job Summary

To develop applied ML and NLP capabilities for understanding healthcare data, the full-time Senior Applied ML Engineer will collaborate with clinical analysts and DME teams to define labels, train models, and ensure domain-model quality in a hybrid work environment.

Key responsibilities:

• Develop schema and document discovery, clinical entity extraction, and data-quality anomaly detection
• Define clinical relevance labels and benchmarks for interoperability recommendations and data model mappings
• Design pilot experiments and analyze performance metrics across source-system families

Required qualifications:

• 5+ years in applied ML, NLP, or equivalent production engineering experience
• Strong proficiency in Python and SQL, along with applied statistics and experimental design
• Hands-on experience with healthcare datasets and clinical terminology
• Experience in curating labels and building evaluation datasets
• Ability to work directly with clinical SMEs and communicate model behavior effectively