Principal Machine Learning Engineer

Overview

Job Title:

Principal Machine Learning Engineer. A rapidly growing healthcare AI company focused on predictive health technology that uses contactless sensing devices and machine learning to detect early signs of patient deterioration. The platform analyzes physiological signals and clinical data to help healthcare providers intervene earlier and prevent avoidable hospitalizations. The role sits at the intersection of applied machine learning, healthcare data, and real-world deployment.

The Principal Machine Learning Engineer will own the end-to-end lifecycle of predictive models that power clinical decision support and operational workflows in production, contribute to improving existing risk prediction models, and explore new ML applications across clinical and biometric data. This is a hands-on, high-impact role requiring solving complex ML problems with real-world consequences and delivering models that perform reliably on messy data in a startup environment.

• Design, train, and continuously improve production-grade machine learning models for predictive risk scoring, clinical classification, and health deterioration detection
• Apply statistical learning approaches including gradient boosting methods (XGBoost, Light
  
  GBM, Cat Boost) as well as modern deep learning approaches (e.g., transformer-based architectures where appropriate)
• Work with time-series and longitudinal datasets derived from physiological signals, vital signs, and operational healthcare data
• Design experiments to evaluate new modeling techniques, feature engineering strategies, and training approaches that improve predictive performance
• Own the full model lifecycle from research and experimentation through validation, production deployment, monitoring, and iteration
• Develop and maintain feature pipelines that transform raw sensor data, clinical indicators, and behavioral signals into model-ready datasets
• Collaborate with clinicians, engineers, and product stakeholders to ensure models are interpretable, clinically useful, and aligned with real-world workflows
• Contribute to exploration of new AI capabilities, including applications of large language models for clinical documentation and workflow optimization
• Investigate new data modalities and signal sources to improve prediction accuracy or enable new product capabilities
• Produce explainability outputs (e.g., SHAP or feature attribution) to support transparency, auditing, and trust in model predictions
• Partner with engineering teams to deploy models into production systems through APIs and scalable pipelines
• Measure real-world impact of models using operational and clinical outcome metrics
• Provide technical leadership in shaping modeling direction and future ML team expansion

• 5–10+ years of experience developing and deploying machine learning models in production environments
• Strong hands-on experience applying statistical and machine learning techniques to real-world datasets
• Experience improving model performance through experimentation, feature engineering, or training optimization
• Advanced Python expertise and experience with ML tooling such as Num Py, pandas, scikit-learn, PyTorch, Tensor Flow, or similar frameworks
• Strong foundation in statistics, machine learning theory, and model evaluation methodologies
• Experience working with structured, tabular, or time-series datasets
• Demonstrated ability to own ML projects end-to-end, from experimentation through deployment and monitoring
• Ability to communicate technical trade-offs and model behavior to cross-functional stakeholders
• Comfort working in ambiguous problem spaces where experimentation and iteration are required
• Experience collaborating with distributed teams across time zones is a plus

• Experience working in healthcare, life sciences, insurance, fintech, or other regulated industries
• Exposure to clinical prediction problems, early warning systems, survival modeling, or anomaly detection
• Experience working with sensor data, physiological signals, or real-world behavioral datasets
• Familiarity with LLM-enabled systems or modern AI-assisted workflows
• Experience evaluating or developing models using deep learning or transformer-based architectures
• Startup experience where ML models directly influenced product outcomes or user workflows
• Publications, patents, or technical writing related to applied machine learning
• Experience mentoring other ML engineers or contributing to technical direction

• Opportunity to build machine learning systems that directly influence real-world healthcare outcomes
• Work in a fast-moving environment where models are deployed quickly and continuously improved
• Direct collaboration with clinicians, engineers, and product leaders solving meaningful healthcare problems
• High ownership role helping shape the future direction of a predictive health platform
• Exposure to diverse machine learning challenges spanning statistical modeling, deep learning, and emerging AI…