Senior Machine Learning Engineer (Medtech)

TL;DR

Senior Machine Learning Engineer (Medtech): Building machine learning systems that analyze large-scale, multi-modal, longitudinal health data to generate actionable insights for Function users with an accent on identifying connections between disparate types of data and analyzing the trajectory of that data over time to provide early warning of disease. Focus on ensuring that ML systems are reliable, interpretable, and suitable for use in regulated settings involving protected health information.

Location: Remote (US, Canada)

Company

hirify.global is the AI operating system for health, designed to empower people to live 100 healthy years.

What you will do

• Develop, train, evaluate, and deploy machine learning models using multimodal healthcare data.
• 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.

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.
• 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.
• 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.
• Experience working in PHI-sensitive and compliance-driven environments.

Culture & 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