Role

We are looking for a Senior Machine Learning Engineer to help us advance our clinical AI and support rapid model experimentation and development. The ideal candidate works exceptionally well across disciplines and delivers high quality outcomes continuously.

SmarterDx builds clinical AI that empowers hospitals to achieve 100% chart accuracy for revenue integrity. Our solution helps hospitals tell the most accurate and complete story of the patient and the care that was delivered, which helps them recover millions in earned revenue and improve quality of care scores. Become a Smartian and help optimize the way the healthcare system works for everyone. Learn more at smarterdx.com/careers.

**This role is fully remote within the US**

What You’ll Do

• Develop, deploy, and maintain machine learning models and pipelines in production environments.
• Work closely with data scientists, data engineers, and application engineers to integrate ML models into the broader SmarterDx platform.
• Craft, implement, and maintain MLOps tools and practices, including continuous integration, delivery, and monitoring of machine learning systems.
• Optimize model performance and scalability, ensuring high reliability and efficiency.
• Build tools to improve the lives of our data scientists.
• Contribute to the design and architecture of our ML systems.

What You Bring

• 5+ years of experience in machine learning engineering or MLOps roles.
• Strong proficiency in Python and SQL, with experience in machine learning libraries (e.g., scikit-learn, PyTorch) and data manipulation (e.g., pandas, SQL).
• Experience with deploying and managing ML models in production environments, including familiarity with Docker, Kubernetes, or similar technologies.
• Understanding of CI/CD best practices for machine learning, including automated testing and deployment pipelines.
• Experience with cloud platforms (preferably AWS) and their machine learning services.
• Familiarity with infrastructure as code (e.g., CDK, Terraform) and configuration management tools.
• Strong understanding of data engineering and architecture principles.
• Excellent collaboration and communication skills, with a passion for solving challenging problems in a team environment.

Nice To Haves

• Experience with end-to-end machine learning project lifecycle, from data collection and model training to deployment and monitoring.
• Deployment of LLM systems into Production environments.
• Contributions to open-source projects or active participation in the machine learning community.
• Previous work in the healthcare sector or related fields.

Our Stack

Python, Scikit-learn, PyTorch, pandas, Airflow, Snowflake, dbt, AWS, Sagemaker, Terraform

Compensation

• $180K to $220K salary + equity