Senior Software Engineer, ML Engineering

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 analyze the complete record of every patient to fully capture the value of care delivered. Founded by physicians in 2020, its proprietary AI platform understands the nuances of clinical reasoning, enabling hospitals to true the patient record for every discharge. By doing so, hospitals can recover millions in earned revenue, enhance care quality metrics, and optimize healthcare operations. 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

• 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. PyTorch, Tensorflow) and data manipulation (e.g., pandas, Polars, 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 Tech Stack

• Python, PyTorch, pandas/Polars, Airflow, Snowflake, dbt, AWS, MLflow, Sagemaker, Terraform, K8s

$180-230k base + equity incentives