Senior Machine Learning Engineer

Your Mission

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.

Who you are

Key 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.

Nice-to-have

• 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.

What’s in it for you?

• 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

Why You'll Love Working With Us: