Machine Learning Engineer - Hello Heart

5+ years of hands-on experience building, deploying, operating, and debugging ML systems in production , with end-to-end ownership from modeling through production. Bachelor's degree in Statistics, Computer Science, Applied Mathematics, Engineering, or a related quantitative field - a strong statistical foundation is essential for this role. Deep expertise in statistics and probability: distributions, inference, hypothesis testing, Bayesian methods, causal inference, and experimental design, with the ability to apply these rigorously in a healthcare context. Strong software engineering skills in Python, with experience writing high-quality, maintainable, well-tested production code. Hands-on experience with CI/CD, real-time or low-latency model serving, monitoring and observability, production debugging, reliability, and scalability. 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. Experience building and owning end-to-end ML pipelines , including feature engineering, model registries, automated evaluation, deployment, and monitoring. Strong ability to translate complex statistical and technical findings into clear insights and recommendations for both technical and non-technical stakeholders. Hello Heart has a positive, diverse, and supportive culture - we look for people who are collaborative, creative, and courageous. Oh, and if you want to see some recent evidence of the fun things we do at Hello Heart, check out our Instagram page .

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.

5+ years of hands-on experience building, deploying, operating, and debugging ML systems in production , with end-to-end ownership from modeling through production. Bachelor's degree in Statistics, Computer Science, Applied Mathematics, Engineering, or a related quantitative field - a strong statistical foundation is essential for this role. Deep expertise in statistics and probability: distributions, inference, hypothesis testing, Bayesian methods, causal inference, and experimental design, with the ability to apply these rigorously in a healthcare context. Strong software engineering skills in Python, with experience writing high-quality, maintainable, well-tested production code. Hands-on experience with CI/CD, real-time or low-latency model serving, monitoring and observability, production debugging, reliability, and scalability. 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. Experience building and owning end-to-end ML pipelines , including feature engineering, model registries, automated evaluation, deployment, and monitoring. Strong ability to translate complex statistical and technical findings into clear insights and recommendations for both technical and non-technical stakeholders. Hello Heart has a positive, diverse, and supportive culture - we look for people who are collaborative, creative, and courageous. Oh, and if you want to see some recent evidence of the fun things we do at Hello Heart, check out our Instagram page .