Machine Learning Engineer

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 bringing models to production, owning all aspects — modeling, code, and deployment. You'll write production-ready code, optimize models for real constraints, and build systems that work at scale. You should be comfortable using AI coding assistants as a core part of your workflow and have a proven track record of shipping models to users.

Responsibilities

• Lead end-to-end development of predictive ML models — from feature engineering, modeling, and training to serving, deployment, and ongoing monitoring. Work across engagement and clinical risk domains.
• Write high-quality, maintainable, well-tested production-grade code, and own its observability, debugging, reliability, and scalability in production.
• Use AI coding assistants to accelerate development, code review, testing, debugging, and documentation
• Partner with product managers, data engineers, and software engineers to translate strategic questions and user behavior patterns into scalable, production-ready, 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.
• Build and maintain production ML infrastructure, including CI/CD, real-time model serving, versioning, evaluation pipelines, monitoring, and observability.
• Design and interpret A/B tests and other experimental methodologies to measure the impact of models, features, and interventions.

Qualifications

• Strong coding skills with experience writing production-grade, maintainable, well-tested code. Hands-on experience with CI/CD, real-time model serving, monitoring, and production debugging,  comfortable owning reliability and scalability.
• 5+ years of end-to-end ownership of ML pipelines — feature engineering, modeling, automated evaluation, deployment, and monitoring in production. Comfort with the statistical concepts needed to use models effectively: distributions, inference, hypothesis testing, and experimental design. Able to translate findings into clear insights and ensure models deliver what we need in a healthcare context.
• Proficiency using AI coding assistants as a core part of the development workflow.
• Expertise with ML frameworks such as PyTorch, scikit-learn, XGBoost, or LightGBM.