Senior Machine Learning Engineer

Join us as we work to create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all. The Senior Machine Learning Engineer is responsible for designing, developing, deploying, and optimizing machine learning solutions that support healthcare products and analytics initiatives across athenahealth. Based in Boston, MA in a hybrid work model, this role partners with cross‑functional teams to apply modern machine learning, data science, and software engineering practices to meaningful healthcare challenges.

The individual in this role will contribute across the full machine learning lifecycle, from identifying opportunities and evaluating approaches to deploying production services and improving model performance over time. This position reports to the Data Science Manager. Team

Summary:

The athena

Clinicals product is a vital component of the athena

One platform, enabling strong experiences for clients and users across clinical workflows. The Data Science team applies machine learning, advanced analytics, and modern engineering practices to automate existing workflows and create more efficient, intelligent solutions. In partnership with product and engineering leaders across the company, the team works to embed machine learning capabilities into athenahealth’s suite of products in ways that improve usability, increase automation, and support innovation.

This team focuses on applying machine learning to complex healthcare problems across a variety of products and domains. Team members work closely with platform engineers and cross‑functional partners to develop, deploy, and scale state‑of‑the‑art machine learning models using cloud technologies and production‑grade engineering practices. Work is typically executed within scrum teams of 2-4 people, with close collaboration across technical and non‑technical stakeholders to deliver practical, measurable outcomes.

• Identify opportunities to apply machine learning techniques to healthcare product and business problems and evaluate which approaches are most appropriate.
• Design and develop machine learning models and ML-based production services for client-facing and internal applications.
• Build scalable data pipelines, feature engineering workflows, and training datasets using structured and unstructured data.
• Deploy and maintain production machine learning services using cloud infrastructure and machine learning operations practices.
• Apply rigorous testing and validation methods to statistics, models, code, and production workflows to support quality and reliability.
• Follow and contribute to conventions and best practices for modeling, coding, architecture, and statistical methods.
• Collaborate effectively with colleagues across technical and non‑technical functions to define requirements, communicate findings, and deliver solutions.
• Contribute to the development of internal tools, reusable frameworks, and team standards that improve the effectiveness of data science work.
• Use artificial intelligence tools to improve experimentation, coding, analysis, and workflow efficiency, while reviewing outputs carefully and applying sound judgment to technical decisions.
• Monitor model and service performance and improve solutions over time based on operational insights, changing requirements, and business impact.

• Support exploratory analyses, proofs of concept, and prototype development for emerging machine learning opportunities.
• Partner with platform and infrastructure teams to improve tooling for model training, deployment, observability, and reproducibility.
• Assist in establishing best practices for experiment tracking, model versioning, feature management, and…