Machine Learning Engineer

About Hello Heart: Hello Heart is on a mission to make heart attacks a thing of the past. We’re an AI company focused exclusively on heart health, building a platform that predicts and prevents cardiac events before they happen—identifying risk up to 10 days in advance versus 10 years in traditional clinical models. This is already working at scale. Hello Heart has been shown to reduce inpatient hospital days by 47% and deliver ~$1,800 in annual savings per member. Hello Heart is the cardiac prevention partner to over 80% of large U.S. health plans and serves hundreds of public and private employers. We’re defining how the #1 cause of death—heart disease—is managed in the AI era. Join us.

About

the Role Hello Heart is seeking a Machine Learning Engineer to join

the team that builds the predictive intelligence powering the Hello Heart app. You will own the ML models behind user engagement, cardiovascular risk stratification, and personalized health recommendations — the systems that determine what users see, when they're nudged, and how their health trajectories are shaped. This role demands both statistical depth and engineering proficiency — you will be expected to take models from research through to deployment, write code built for production, and use AI coding assistants fluently as part of how you get work done.

Responsibilities Lead end-to-end development of predictive ML models. From data exploration and feature engineering through training, validation, deployment, and ongoing monitoring across engagement and clinical risk domains. Apply strong statistical foundations to model design, feature selection, uncertainty quantification, and interpretation of results Write production-grade Python code that is clean, tested, and built for maintainability and scale. Use AI coding assistants to accelerate development, code review, and documentation without sacrificing quality or rigor. Partner with product managers, data engineers, and software engineers to translate strategic questions and user behavior patterns into measurable, data-driven solutions. Research and implement cutting-edge ML techniques spanning supervised and unsupervised learning, causal inference, deep learning, and reinforcement learning to tackle complex healthcare challenges. Contribute to MLOps infrastructure: model serving, versioning, evaluation pipelines, and monitoring. Design and interpret A/B tests and other experimental methodologies to measure the impact of models, features, and interventions.