Senior Machine Learning Engineer

athenahealth•Boston, MA

1d•$145,000 - $247,000•Hybrid

About The Position

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 athenaClinicals product is a vital component of the athenaOne 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.

Requirements

Bachelor’s or Master’s degree in Mathematics, Computer Science, Data Science, Statistics, or a related quantitative field, or equivalent practical experience.
4 to 6 years of professional hands-on experience developing, evaluating, and deploying machine learning models in production environments.
Proficiency in Python, Structured Query Language (SQL), and Unix-based development environments.
Experience building, testing, and maintaining production-grade machine learning services and workflows.
Knowledge of machine learning fundamentals, statistical methods, model evaluation, and software engineering best practices.
Familiarity with natural language processing, computer vision, or other applied machine learning techniques.
Strong communication skills, including the ability to communicate clearly in writing and in conversation with technical and non-technical audiences.

Nice To Haves

Experience with deep learning models and complex neural network architectures is helpful.
Experience training or fine-tuning large language models and generative artificial intelligence models is helpful.
Experience with cloud platforms such as Amazon Web Services, including technologies such as Kubernetes, Kubeflow, or Elastic Kubernetes Service, is helpful.

Responsibilities

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 continuous integration and continuous deployment.
Prepare technical summaries, recommendations, and presentations for stakeholders across a range of technical backgrounds.
Evaluate new tools, frameworks, and methodologies relevant to machine learning engineering, data science, and generative artificial intelligence.
Participate in incident analysis and remediation efforts related to machine learning-enabled systems.
Provide technical guidance and knowledge sharing to peers through collaboration, feedback, and documentation.
Contribute to roadmap planning, estimation, and prioritization for machine learning and data science initiatives.