Principal Machine Learning Engineer

Role Overview

A rapidly growing healthcare AI company is transforming how clinicians monitor and care for vulnerable populations. The organization builds predictive health technology that combines contactless sensing devices with advanced machine learning to detect early signs of patient deterioration. Their platform analyzes physiological signals and clinical data to help healthcare providers intervene earlier and prevent avoidable hospitalizations.

With thousands of patients monitored daily across post-acute care environments, the company is expanding its engineering team to further advance the predictive models at the core of its platform. This 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 used in production environments. This individual will contribute to both improving existing risk prediction models and exploring new applications of machine learning across clinical and biometric datasets.

This is a hands-on, high-impact role for someone who enjoys solving complex ML problems with real-world consequences, building models that must perform reliably on messy real-world data, and rapidly iterating in a startup environment where shipped models directly affect patient outcomes.

Key Responsibilities

• 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 (such as XGBoost, LightGBM, CatBoost) as well as modern deep learning approaches including 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 closely 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 (LLMs) for clinical documentation and workflow optimization
• Investigate new signal sources and data modalities that may improve prediction accuracy or enable new product capabilities
• Produce explainability outputs (such as 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
• Contribute technical leadership in shaping modeling direction and future ML team expansion

Education & Qualifications

• 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 NumPy, pandas, scikit-learn, PyTorch, TensorFlow, 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

Preferred Experience

• 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

Why Join

• 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 technologies
• Strong growth trajectory with increasing demand for predictive healthcare technologies

Benefits and Perks

• Competitive base salary range: $160,000 – $260,000 plus meaningful equity participation
• 100% company-paid medical, dental, and vision coverage
• 401(k) with employer match
• Generous paid time off
• Collaborative headquarters workspace with team events and weekly team lunches
• Opportunity to work on technology that directly impacts patient care and healthcare outcomes

Applicants must be currently authorized to work in the United States on a full-time basis now and in the future. This position does not offer sponsorship.