Senior Machine Learning Engineer

Function Health is an innovative company focused on redefining health through AI technology. As a Senior Machine Learning Engineer, you will develop and deploy machine learning systems that analyze health data to provide actionable insights for users, collaborating with cross-functional teams to ensure reliability and interpretability of the models.

Responsibilities:

• Develop, train, evaluate, and deploy machine learning models using multimodal healthcare data (e.g., blood biomarkers, images, medical records)
• Partner with data scientists and domain experts to translate clinically informed cohorts, labels, and features into ML-ready representations
• Build and own end-to-end ML workflows, including literature review/prototyping, feature generation, training/validation, inference, experiment tracking and reproducibility, deployment, and monitoring/drift detection
• Design modeling approaches for longitudinal healthcare data, capturing temporal patterns and handling evolving data distributions
• Define evaluation frameworks that prioritize robustness, calibration, interpretability, and stability across cohorts and time
• Contribute to best practices around responsible ML in healthcare, including documentation, auditability, and collaboration with clinical stakeholders
• Support experimentation while maintaining production-quality engineering standards

Requirements:

• 3+ years of experience building and deploying machine learning systems in production
• Strong proficiency in Python and ML frameworks, such as PyTorch, TensorFlow, and scikit-learn (PyTorch is preferred)
• Experience with the full model lifecycle: training, evaluation, deployment, and monitoring
• Familiarity with multimodal and/or longitudinal/time-series data (tabular biomarkers, imaging-derived features, events over time, etc.)
• Solid understanding of feature engineering, model validation, error analysis, and basic statistical thinking
• Ability to collaborate effectively with data engineering and data scientists in shared data environments
• Experience working with healthcare, biomedical, or other regulated data
• Familiarity combining multiple different modalities (e.g., tabular + imaging features, signals + clinical records)
• Experience with self-supervised learning and the development of large-scale foundation models
• Experience deploying models in cloud environments (AWS, Databricks, etc.)
• Exposure to model interpretability techniques and monitoring strategies (drift, performance degradation, data quality checks)
• Experience working in PHI-sensitive and compliance-driven environments