Senior Machine Learning Engineer, ML Ops

About the Role:
Hello Heart is looking for a Senior ML Engineer/ML Ops to own the engineering behind the models in the Hello Heart app: member engagement, cardiovascular risk stratification, and personalized health recommendations. You'll be the first engineer dedicated to ML on our Data Platform team, and you'll own the full model lifecycle: training pipelines, the model registry, automated evaluation and release, real-time and batch serving, monitoring, and retraining. You'll work closely with the data scientists who own the modeling and with the product engineers who call the models, so that what they build ships safely and runs reliably. Our models run on Snowflake ML. Our ML platform is still taking shape, so you'll set the standards rather than inherit them. You should have a proven record of owning ML systems in production and be comfortable using AI coding assistants as a core part of your workflow.

Responsibilities:

•  Turn model releases into a pipeline: every new version is evaluated automatically, gated against the one in production, approved, and rolled out with a fast rollback.
• Build drift and performance monitoring for our production models, and the retraining that follows from it.
• Own the model lifecycle end to end: training pipelines, versioning and the model registry, automated evaluation, promotion and rollback, deployment, monitoring, and retraining.
• Build CI/CD for ML: automated testing and validation of every model version, release gates with clear criteria, and an audit trail of who shipped what and why.
• Serve models in real time through our Python model-serving service and Snowflake Container Services, with a focus on low latency, reliability, and graceful degradation. Run batch inference in the warehouse with clear standards for latency, correctness, and cost.
• Build and operate ML pipelines in Airflow and Snowflake, from feature preparation through training and batch scoring, with reproducible training data.
• Monitor drift, data quality, latency, and model performance, and automate retraining that goes through the same release gate.
• Partner with data scientists, product engineers, and data engineers. Define clear interfaces between modeling, serving, and the services that consume models, and push back when a request belongs elsewhere.
• Handle members' health data responsibly: access control, pseudonymization, and nothing sensitive in logs.
• Write high-quality, well-tested production code, and own its observability, debugging, and reliability.

Requirements:

• 5+ years of software engineering, including 3+ years of hands-on MLOps owning production ML systems on AWS with a data or ML platform (e.g., Snowflake ML, Databricks, SageMaker, Vertex AI).
• Hands-on model management: versioning, registries, automated evaluation, and promotion and rollback of models in production.
• Experience building CI/CD and pipelines for ML: automated training, validation, and deployment (e.g., Jenkins, GitHub Actions), with orchestration in Airflow or similar.
• Strong Python experience, including building and operating Python services (APIs or gateways) in production.
• Experience serving models in real time: low-latency inference, autoscaling, and reliable serving architectures.
• Strong SQL and data warehouse experience (Snowflake preferred).
• Experience monitoring models in production (drift, data quality, and performance) and acting on what you find.
• Experience owning a solution end to end, from pipeline and model integration through deployment and production debugging.
• Proficiency with AI coding assistants as a core part of your workflow, and the judgment to review and verify what they produce.

Nice-to-Haves:

• Snowflake ML: Model Registry, Snowpark Container Services, ML Observability, or Notebooks and ML Jobs.
• Shadow and canary releases for models.
• Feature stores and ML lifecycle tooling (e.g., Feast, MLflow, Weights & Biases).
• Model development: feature engineering, training, and evaluation with scikit-learn, XGBoost, or LightGBM.
• Kubernetes and infrastructure as code (Terraform).
• Event-driven systems (Kafka).
• Integrating LLMs into production systems.
• Observability stacks (Prometheus, Grafana, Datadog).
• Experience with health or other regulated data (HIPAA).