Asks for

The ad says“Proficiency using AI coding assistants as a core part of the development workflow.”
The ad says“Build and own production ML infrastructure, including CI/CD pipelines, real-time model serving, model versioning and registries, automated evaluation pipelines, monitoring, and observability.”
The ad says“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).”
The ad says“Build and own production ML infrastructure, including CI/CD pipelines, real-time model serving, model versioning and registries, automated evaluation pipelines, monitoring, and observability.”
The ad says“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.”
The ad says“Senior Machine Learning Engineer, MLOps”
The ad says“Build and own production ML infrastructure, including CI/CD pipelines, real-time model serving, model versioning and registries, automated evaluation pipelines, monitoring, and observability.”
The ad says“Build and own production ML infrastructure, including CI/CD pipelines, real-time model serving, model versioning and registries, automated evaluation pipelines, monitoring, and observability.”
The ad says“Build and own production ML infrastructure, including CI/CD pipelines, real-time model serving, model versioning and registries, automated evaluation pipelines, monitoring, and observability.”
The ad says“Strong Python skills, with production-grade, maintainable, well-tested code.”
The ad says“Build and own production ML infrastructure, including CI/CD pipelines, real-time model serving, model versioning and registries, automated evaluation pipelines, monitoring, and observability.”

Values

The ad says“Experience with workflow orchestration tools (Airflow, Dagster, Prefect, Kubeflow Pipelines).”
The ad says“Hands-on experience integrating LLMs into production systems (e.g., LangChain, LangGraph, AWS Bedrock, RAG).”
The ad says“Experience with workflow orchestration tools (Airflow, Dagster, Prefect, Kubeflow Pipelines).”
The ad says“Experience with monitoring/observability stacks (Prometheus, Grafana, Datadog).”
The ad says“Familiarity with ML lifecycle tooling: experiment tracking, model registries, feature stores (MLflow, DVC, Feast, Weights & Biases).”
The ad says“Familiarity with ML lifecycle tooling: experiment tracking, model registries, feature stores (MLflow, DVC, Feast, Weights & Biases).”
The ad says“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).”
The ad says“Experience with monitoring/observability stacks (Prometheus, Grafana, Datadog).”
The ad says“Experience with workflow orchestration tools (Airflow, Dagster, Prefect, Kubeflow Pipelines).”
The ad says“Hands-on experience integrating LLMs into production systems (e.g., LangChain, LangGraph, AWS Bedrock, RAG).”
The ad says“Hands-on experience integrating LLMs into production systems (e.g., LangChain, LangGraph, AWS Bedrock, RAG).”
The ad says“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).”
The ad says“Hands-on experience integrating LLMs into production systems (e.g., LangChain, LangGraph, AWS Bedrock, RAG).”
The ad says“Familiarity with ML lifecycle tooling: experiment tracking, model registries, feature stores (MLflow, DVC, Feast, Weights & Biases).”
The ad says“Experience with workflow orchestration tools (Airflow, Dagster, Prefect, Kubeflow Pipelines).”
The ad says“Experience with monitoring/observability stacks (Prometheus, Grafana, Datadog).”
The ad says“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).”
The ad says“Hands-on experience integrating LLMs into production systems (e.g., LangChain, LangGraph, AWS Bedrock, RAG).”
The ad says“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).”
The ad says“Familiarity with ML lifecycle tooling: experiment tracking, model registries, feature stores (MLflow, DVC, Feast, Weights & Biases).”
The ad says“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).”

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-Haves

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.