Senior Machine Learning Engineer, MLOps

Job description

**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 **and production ownership** of predictive ML models, from data exploration and feature engineering through training, validation, **deployment, real-time serving,** 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 **high-quality, maintainable, well-tested production-grade Python code, and own its observability, debugging, reliability, and scalability in production.** - Use AI coding assistants to accelerate development, code review, **testing, debugging,** 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 **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** - 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](https://www.instagram.com/helloheartapp/).