AI-ML Engineer - Finance-Hybrid

Mayo Clinic•Rochester, MN

1d•$116,043 - $142,147•Hybrid

About The Position

This hybrid AI/ML Engineer role is in Mayo Clinic’s Financial and Accounting Services group, which primarily focuses on time series analytics and generative AI solutions for finance applications rather than clinical applications. The position must be located within 100 miles of a Mayo Clinic campus to meet on-site expectations based on business needs. The role works across the AI life cycle, from ideation through production, developing, integrating, and standardizing software components and supporting quality system procedures. It includes engineering systems and processes for AI solution design, development, verification, non-clinical validation, and risk mitigation, as well as designing, testing, and maintaining tools and CI/CD pipelines for consistent, automated development and releases. The posting also describes collaboration with multidisciplinary teams, analysis of complex data, and education for healthcare staff on AI tools and technologies. The position description discusses clinical AI and healthcare workflows, although the group is identified as finance-focused. Mayo Clinic offers competitive compensation, comprehensive benefits, and continuing education and advancement opportunities. The position is not eligible for visa sponsorship or transfer.

Requirements

A master’s degree in engineering, computer science, mathematics, health science, or a related field and 1 year of experience, or a bachelor’s degree and 3 years of experience.
Experience applying AI and machine learning in production environments or similar regulated or technology-focused industries, with an understanding of healthcare technology.
Skill in cloud infrastructure environments and software development tools.
Experience working with large, complex, heterogeneous data sets, preferably in healthcare.
Skill in AI/ML techniques and frameworks.
History of collaboration across diverse teams and communicating complex technical concepts to non-technical stakeholders.
Familiarity with best practices in data engineering, data science, AI engineering, and MLOps.
Strong interpersonal, communication, and time-management skills.

Nice To Haves

Expertise delivering time-series analytical models and/or agentic solutions in Copilot Studio or Google.
A Ph.D. or other doctorate degree.
Expertise in AI/ML techniques and frameworks such as deep learning, natural language processing, and generative AI, with proficiency in tools such as Python, TensorFlow, PyTorch, scikit-learn, or Keras.
Knowledge of healthcare, including clinical workflows, electronic health records, medical terminologies, regulatory requirements, and industry standards.
Familiarity with systems or quality engineering best practices, regulatory standards, and compliance frameworks.
Expertise in user-centered design, human factors engineering, usability testing, and evaluation across AI product development; ability to conduct expert reviews using established usability practices and communicate findings clearly.
Ability to explain complex technical concepts to technical and non-technical audiences.
Ability to manage varied project workloads and multiple priorities while staying current on healthcare trends.
Experience with healthcare informatics standards, best practices, and common data models.

Responsibilities

Design, develop, integrate, and standardize components for AI-driven solutions.
Collaborate with clinicians, user experience designers, product managers, IT professionals, and other partners to understand user needs, workflows, and requirements; assess feasibility and translate feedback into design concepts and usability specifications.
Interpret and analyze data to inform strategic decisions and communicate findings in understandable terms.
Use machine learning techniques, including deep learning, natural language processing, computer vision, and large language models, to design, develop, and deploy end-to-end AI solutions.
Participate in engineering systems that support the development and deployment of AI solutions.
Design, test, and maintain tools and associated CI/CD pipelines to facilitate consistent and automated AI software development and releases.
Contribute to best practices and standards for AI development and deployment methodologies, tools, and platforms.
Provide training and education to healthcare staff on AI tools and technologies.
Create, maintain, and follow quality system procedures; support verification, non-clinical validation, and risk mitigation.