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 Senior Machine Learning Engineer with strong MLOps abilities to own the production ML systems behind user engagement, cardiovascular risk stratification, and personalized health recommendations in the Hello Heart app. You'll build and run the CI/CD, real-time serving, versioning, and monitoring infrastructure that models depend on, working closely with data scientists who own the modeling to make sure what they build runs reliably at scale. You should have a proven track record of shipping ML systems to production and be comfortable using AI coding assistants as a core part of your workflow.
Responsibilities:
- Build and own production ML infrastructure, including CI/CD pipelines, real-time model serving, model versioning and registries, automated evaluation pipelines, monitoring, and observability.
- Serve models in real time at scale, with a strong focus on low-latency inference, reliability, and graceful degradation under load, and operate batch inference with clear standards for latency, availability, correctness, and cost.
- Write high-quality, maintainable, well-tested production-grade code, and own its observability, debugging, reliability, and scalability in production.
- Implement model monitoring for drift, data quality, latency, and performance degradation, with automated retraining.
Requirements:
- 4+ years of software engineering experience, including 2+ years of hands-on MLOps, building and owning production ML infrastructure on AWS or GCP with a data or ML platform (e.g., Snowflake ML, Databricks, SageMaker, Vertex AI, JFrog ML).
- Strong Python skills, with production-grade, maintainable, well-tested code.
- Hands-on experience building CI/CD pipelines for ML: automated testing, training, validation, and deployment of models (e.g., Cloud Build, Cloud Deploy, Jenkins, GitHub Actions).
- Experience serving models in real time at scale: low-latency inference, autoscaling, and reliable serving architectures.
- Experience owning a solution end-to-end, from pipeline and model integration through deployment and production debugging.
- Proficiency using AI coding assistants as a core part of the development workflow.
Nice-to-Have:
- Hands-on model development experience: feature engineering, training, and evaluation, with frameworks such as PyTorch, scikit-learn, XGBoost, or LightGBM (plus YOLO/OCR, supervised/unsupervised learning).
- Hands-on experience integrating LLMs into production systems (e.g., LangChain, LangGraph, AWS Bedrock, RAG).
- Familiarity with ML lifecycle tooling: experiment tracking, model registries, feature stores (MLflow, DVC, Feast, Weights & Biases).
- Experience with workflow orchestration tools (Airflow, Dagster, Prefect, Kubeflow Pipelines).
- Experience with monitoring/observability stacks (Prometheus, Grafana, Datadog).
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.
Skills Required
- 5+ years developing, deploying, and maintaining ML models in production environments
- Bachelor's degree in Statistics, Computer Science, Applied Mathematics, Engineering, or related quantitative field
- Deep expertise in statistics and probability, including inference, hypothesis testing, Bayesian methods, causal inference, and experimental design
- Strong software engineering skills in Python: production-grade practices, version control, testing, and reproducibility
- 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 or working within end-to-end ML pipelines, including feature engineering, model registries, deployment tooling, and monitoring
- Strong ability to translate complex statistical and technical findings into clear insights for technical and non-technical stakeholders
- Experience with cloud platforms (AWS preferred), containerization (Docker, Kubernetes), and MLOps platforms
- Prior work with healthcare or clinical datasets, including wearable device data, EMR, or claims data
- Experience with recommendation systems, reinforcement learning, or advanced causal inference
Hello Heart Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Hello Heart and has not been reviewed or approved by Hello Heart.
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Healthcare Strength — Healthcare benefits are considered strong, with an employer-verified health insurance rating of 4.7/5 and a benefits rating of 3.9/5 on one platform. Feedback suggests medical, dental, and vision coverage is comprehensive for employees and dependents.
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Equity Value & Accessibility — Equity/stock options are commonly included in compensation packages, signaling accessible ownership opportunities typical of venture-backed startups. This element is often cited alongside base pay as part of a competitive total rewards mix.
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Leave & Time Off Breadth — Time off policies are characterized as flexible or unlimited PTO, encouraging rest and balance. This breadth of leave is presented as part of a comprehensive startup package.
Hello Heart Insights
What We Do
Hello Heart is the only digital therapeutics company to focus exclusively on heart disease, the #1 cause of death for men and women in the US. Through a connected mobile app that uses AI, behavioral science, and personalized digital coaching to drive lifestyle changes, Hello Heart empowers people to embrace healthier behavior, which can reduce the risks of high blood pressure and heart disease. It also helps users catch readings that are extremely high, encourage them to talk to their doctor and catch potential risk in time. Validated in peer-reviewed studies, Hello Heart is easy to use and works alongside an employer’s benefits ecosystem. Founded in 2013, Hello Heart is a member of the American Heart Association’s Innovators’ Network, and is part of the CVS Health Point Solutions Management Program. Visit www.helloheart.com for more information.
