Job description

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. Essential Job 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. Additional Job Responsibilities: 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. Expected Education & Experience: 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. 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. Strong communication skills, including the ability to communicate clearly in writing and in conversation with technical and non-technical audiences. Expected Compensation $145,000 - $247,000 The base salary range shown reflects the full range for this role from minimum to maximum. At athenahealth, base pay depends on multiple factors, including job-related experience, relevant knowledge and skills, how your qualifications compare to others in similar roles, and geographical market rates. Base pay is only one part of our competitive Total Rewards package - depending on role eligibility, we offer both short and long-term incentives by way of an annual discretionary bonus plan, variable compensation plan, and equity plans. About athenahealth Our vision: In an industry that becomes more complex by the day, we stand for simplicity. We offer IT solutions and expert services that eliminate the daily hurdles preventing healthcare providers from focusing entirely on their patients — powered by our vision to create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all. Our company culture: Our talented    employees — or athenistas, as we call ourselves — spark the innovation and passion needed to accomplish our vision. We are a diverse group of dreamers and do-ers with unique knowledge, expertise, backgrounds, and perspectives. We unite as mission-driven problem-solvers with a deep desire to achieve our vision and make our time here count. Our award-winning culture is built around shared values of inclusiveness, accountability, and support. Our DEI commitment: Our vision of accessible, high-quality, and sustainable healthcare for all requires addressing the inequities that stand in the way. That s one reason we prioritize diversity, equity, and inclusion in every aspect of our business, from attracting and sustaining a diverse workforce to maintaining an inclusive environment for athenistas, our partners, customers and the communities where we work and serve. What we can do for you: Along with health and financial benefits, athenistas enjoy perks specific to each location, including commuter support, employee assistance programs, tuition assistance, employee resource groups, and collaborative    workspaces   — some offices even welcome dogs. We also encourage a better work-life balance for athenistas with our flexibility. While we know in-office collaboration is critical to our vision, we recognize that not all work needs to be done within an office environment,   full-time. With consistent communication and digital collaboration tools, athenahealth   enables   employees to find a balance that feels fulfilling and productive for each individual situation. In addition to our traditional benefits and perks, we sponsor events throughout the year, including book clubs, external speakers, and hackathons. We provide athenistas with a company culture based on learning, the support of an engaged team, and an inclusive environment where all employees are valued. Learn more about our culture and benefits here: athenahealth.com/careers athenahealth.com

Requirements

Must-haves

Join us as we work to create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all.
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.
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.

Nice-to-haves

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Application prep

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Keywords to mirror

machine learningscrummlpythonsqlkubernetes

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Vision