Principal Machine Learning Engineer (4620)

Job Title: 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.