Senior AI/ML Engineer, epocrates

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

Role summary:
The Senior AI/ML Engineer, epocrates, will help design and deliver AI and machine learning solutions that support high-value product experiences for clinicians. The position partners across engineering, product, and adjacent teams to move AI/ML capabilities from research and prototype stages into reliable production use. This person will report to the Senior Engineering Manager.

Team summary:
athenahealth’s epocrates team is seeking a Senior AI/ML Engineer to help build AI and machine learning capabilities that support the platform and mobile products used by more than one million healthcare professionals at the point of care. The team is focused on delivering practical, scalable, and trustworthy AI capabilities that improve how users access information, interact with product experiences, and benefit from new clinical and workflow enhancements.

As a contributor on the AI Capabilities engineering team, you will work across the full AI/ML lifecycle, from research and design through deployment, monitoring, and maintenance. The work combines data science, machine learning engineering, and software engineering, with responsibility for building production-ready systems that can scale reliably and operate with clear guardrails.

This team is also responsible for helping establish the foundation for safe and effective AI use across epocrates, including standards for deployment, monitoring, and governance. In this role, you will collaborate with other engineers and cross-functional partners to evaluate approaches, prototype new techniques, and translate ideas into solutions that align with business goals and customer needs.

The environment values practical experimentation, sound engineering judgment, and clear communication. You will also help advance responsible AI practices by considering fairness, transparency, privacy, and the operational impact of models in production.

Essential Job Responsibilities:

• Participate in end-to-end AI/ML projects, from research and design through deployment and ongoing maintenance, to deliver scalable production systems.

• Implement ML pipelines and production-grade infrastructure that support high-volume workloads and reliable model execution.

• Partner with cross-functional teams to integrate AI capabilities that align with business goals and customer needs.

• Research and prototype new AI/ML algorithms and techniques to support innovation and product development.

• Help establish best practices for AI/ML development, deployment, monitoring, and governance across the team.

• Monitor model performance, scalability, and cost in production and contribute to continuous optimization.

• Apply AI tools and techniques in day-to-day engineering work to improve productivity, support experimentation, and strengthen evaluation of model behavior, outputs, and system impact.

• Advocate for responsible AI by considering fairness, transparency, data privacy, and appropriate use in model design and deployment.

• Collaborate with engineering partners to move models from prototype to production using repeatable, maintainable approaches.

• Document technical decisions, model behavior, and operational considerations to support shared understanding and long-term maintainability.

Additional Job Responsibilities:

• Support experimentation with emerging AI methods, including generative AI and agent-based patterns, where appropriate for the product use case.

• Contribute to technical discussions on architecture, tooling, and workflow improvements for AI/ML delivery.

• Assist in evaluating tradeoffs between accuracy, latency, cost, and maintainability.

• Participate in code reviews, design reviews, and operational readiness activities.

• Help maintain shared standards for testing, observability, and release readiness.

• Partner with peers to identify opportunities to improve data quality, pipeline reliability, and model performance.

• Support troubleshooting and issue resolution for models and services in production.

• Stay current on relevant AI/ML developments and share practical insights with the team.

Expected Education & Experience:

• Bachelor’s Degree in Data Science, Mathematics, Statistics, Operations Research, Computer Science/Engineering, or a related technical field.

• 4–6 years of professional experience in software engineering, with exposure to software development, data science, or AI/ML work.

• Experience contributing to AI/ML projects across the model lifecycle, including research, prototyping, deployment, or monitoring.

• Foundational knowledge of classical AI/ML algorithms, deep learning, Natural Language Understanding and Processing, and Generative AI concepts.

• Familiarity with large language models, GPT, BERT, LangChain, and LangGraph concepts and applications.

• Experience or exposure to building and deploying AI/ML systems in AWS.

• Proficiency in Python and working knowledge of a JVM language/service.

• Proficiency in working with SQL and non-SQL data.

• Experience with at least one major ML framework, such as TensorFlow or PyTorch.

• Exposure to MLOps and machine learning workflow orchestration; experience with AWS SageMaker is a plus.

• Strong communication and interpersonal skills, with the ability to work effectively across technical and non-technical partners.

• Interest in staying current with AI research and applying new techniques responsibly in production.

• Healthcare technology experience is a plus, but not required

Expected Compensation

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.