[Hiring] Senior Machine Learning Engineer @Function Health

Role Description

As a Machine Learning Engineer in MIL, you will build machine learning (ML) systems that analyze large-scale, multi-modal, longitudinal health data to generate actionable insights for Function users. These ML systems will identify connections between disparate types of data and will analyze the trajectory of that data over time to provide early warning of disease.

You will collaborate closely with data engineers, data scientists, and clinical domain experts to ensure that ML systems are reliable, interpretable, and suitable for use in regulated settings involving protected health information.

What you’ll do

• 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.

Qualifications

• 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.

Requirements

• Experience working with healthcare, biomedical, or other regulated data (nice-to-have).
• Familiarity combining multiple different modalities (e.g., tabular + imaging features, signals + clinical records) (nice-to-have).
• Experience with self-supervised learning and the development of large-scale foundation models (nice-to-have).
• Experience deploying models in cloud environments (AWS, Databricks, etc.) (nice-to-have).
• Exposure to model interpretability techniques and monitoring strategies (drift, performance degradation, data quality checks) (nice-to-have).
• Experience working in PHI-sensitive and compliance-driven environments (nice-to-have).

Benefits

• Stock options
• Comprehensive health, dental, and vision plans for you and your family
• Wellness and commuter benefits
• Competitive vacation policy
• A culture that emphasizes learning, collaboration, and thoughtful engineering
• Remote work flexibility