AI-ML Engineer - Finance-Hybrid

Mayo Clinic; Rochester (MN); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Mayo Clinic is seeking an AI/ML Engineer within Financial and Accounting Services. This hybrid role, located within 100 miles of Mayo Clinic campuses, focuses on time series analytics and Generative AI solutions tailored for finance—distinct from clinical work.

You will collaborate with clinicians, designers, PMs, IT, and external partners to develop data-driven AI products that improve financial workflows and operational efficiency.

Qualifications

• Experience with AI/ML in healthcare context is desirable.
• Familiarity with clinical workflows and regulatory considerations.
• Ability to translate user needs into AI solutions.

Responsibilities

• Design, develop, integrate, and standardize AI components for healthcare use.
• Collaborate with clinicians, designers, PMs, IT staff, and partners to capture user needs.
• Analyze data to inform decisions and communicate findings clearly.
• Apply deep learning, NLP, computer vision, and large language models to healthcare applications.
• Contribute to building and deploying AI systems and CI/CD pipelines.
• Support best practices and standards for AI development and deployment.
• Provide training to healthcare staff on AI tools and technologies.

Skills

Time series analytics

Generative AI

AI/ML engineering

CI/CD pipelines

Data interpretation

Job description

This is a hybrid position and must be located within 100 miles of any of the Mayo Clinic campuses for on-site expectations based on business needs.

This role sits within Financial and Accounting Services, and this group is primarily focused on time series analytics and Generative AI Solutions tailored for finance applications, not clinical.

AI/ML Engineers at Mayo Clinic play a pivotal role in the union of data, systems, and computer sciences. They work closely with a multidisciplinary team, including clinicians, user experience designers, product managers, IT professionals, and external partners, to develop and deploy effective, efficient, and ethical AI/ML solutions into clinical practice to enhance patient care and operational efficiency.

As an AI/ML Engineer, you may work on the full spectrum of the AI life cycle from ideation to production. You understand the clinical environment well, including workflows, challenges, and requirements of healthcare providers and patients. You will leverage advanced techniques in AI/ML to analyze vast amounts of healthcare data, including patient records, medical imaging, and genomic information, to develop AI solutions that meet clinical needs and are integrated smoothly into clinical processes. You will develop, integrate, and standardize software components and create, maintain, and follow quality system procedures. You will work on the engineering of systems that are pivotal to developing and deploying these solutions, which encompass everything from design requirements, development, component creation, verification, non-clinical validation, and risk mitigation to ensure our digital health technology products meet and exceed regulatory requirements and setting new benchmarks for safety and effectiveness in clinical settings. Your expertise will also extend to facilitating consistent and automated AI software solution development and releases through the design, testing, and maintenance of tools and associated CI/CD pipelines.

• Working on component design, development, integration, and standardization to create AI-driven solutions that seamlessly integrate into clinical practice to enhance patient care and clinic operations.
• Collaborating with a multidisciplinary team, including clinicians, user experience designers, product managers, and IT professionals, to understand user needs, workflows, and clinical requirements and assess feasibility. Translating user feedback and requirements into design concepts and usability specifications for AI solutions.
• Interpreting / analyzing data to inform strategic decisions and communicate complex findings in easily understandable terms to bridge the gap between AI technologies and clinical applications.
• Leveraging machine learning techniques such as deep learning, natural language processing, computer vision, large language models, etc., to design, develop and deploy end-to-to-end AI solutions for healthcare applications.
• Participating in the engineering of systems crucial for developing and deploying AI solutions.
• Facilitating consistent and automated AI software solution development and releases through the design, testing, and maintenance of tools and associated CI/CD pipelines.
• Contributing to implementing the best practices and standards for AI development and deployment methodologies, tools, and platforms.
• Providing training and education to healthcare staff on the use of AI tools and technologies.