Senior Software Engineer, AI/ML

Role Description

Help shape the future of intelligent healthcare engagement through production-ready AI and machine learning solutions. The Senior ML/AI Engineer is a Senior Associate-level role based in the Remote-US in a remote work model, responsible for designing, developing, deploying, and maintaining scalable AI/ML systems that support product innovation across the epocrates platform and mobile applications. This role partners with cross-functional teams to translate business goals and customer needs into practical AI capabilities while advancing responsible, reliable, and efficient ML delivery. This role reports to the Senior Engineering Manager.

athenahealth epocratesTM is seeking a Senior ML/AI Engineer to design, develop, and deploy advanced AI and ML solutions that help transform how pharmaceutical brands connect with more than 1 million healthcare professionals at the moment of care.

• Develop end-to-end AI and ML solutions from research and design through deployment, monitoring, and ongoing maintenance.
• Build scalable ML pipelines and production-grade infrastructure to support high-volume workloads and reliable model execution.
• Partner with cross-functional teams to integrate AI capabilities aligned to business goals, product priorities, and customer needs.
• Research and prototype new AI/ML techniques to support innovation and improve product capabilities.
• Implement best practices for AI/ML development, deployment, monitoring, and governance across the development lifecycle.
• Monitor model performance, scalability, and cost in production, and improve systems based on operational insights.
• Design and deploy AI/ML systems in AWS environments with attention to reliability, maintainability, and production readiness.
• Apply responsible AI practices by considering fairness, transparency, privacy, and appropriate use of data in solution design and delivery.
• Use AI tools and emerging ML technologies in day-to-day engineering work to improve experimentation, development efficiency, and model delivery.

Qualifications

• Bachelor’s degree in Data Science, Mathematics, Statistics, Operations Research, Computer Science, Computer Engineering, or a related technical field, or equivalent professional experience.
• 4 to 6 years of experience in technology or software development, including experience in ML/AI or data science-related roles.
• Technical knowledge of classical AI/ML algorithms, deep learning, natural language understanding and processing, generative AI, large language models, and frameworks such as LangChain and LangGraph.
• Experience developing and deploying scalable AI/ML systems and pipelines in production using AWS.
• Proficiency in Python and working knowledge of JVM-based languages or services.
• Experience working with SQL and non-SQL data stores.
• Experience with at least one major ML framework such as TensorFlow or PyTorch.
• Experience taking models from prototype to production; familiarity with AWS SageMaker is a plus.
• Experience with MLOps practices and machine learning workflow orchestration.
• Strong communication skills and the ability to explain technical concepts clearly to cross-functional partners.

Requirements

• Contribute to technical design reviews and architecture discussions for AI/ML-enabled features.
• Document model assumptions, implementation details, deployment workflows, and operational guidance.
• Evaluate new frameworks, tools, and vendor capabilities for potential use in the AI/ML ecosystem.
• Support troubleshooting and incident response for AI/ML services in production.
• Collaborate with stakeholders to refine use cases, define success metrics, and assess feasibility.
• Share knowledge across teams to strengthen AI/ML understanding and adoption of sound engineering practices.
• Assist with workflow orchestration and MLOps improvements to streamline delivery and maintenance.
• Contribute to continuous improvement of platform standards, monitoring, and governance processes.

Benefits

• Health and financial benefits.
• Perks specific to each location, including commuter support, employee assistance programs, tuition assistance, employee resource groups, and collaborative workspaces.
• Encouragement of a better work-life balance with flexibility in work arrangements.
• Sponsorship of events throughout the year, including book clubs, external speakers, and hackathons.
• A company culture based on learning, support of an engaged team, and an inclusive environment where all employees are valued.