Senior Principal AI​/ML Engineer - Deployment and Integrations

Why Mayo Clinic

Mayo Clinic is top-ranked in more specialties than any other care provider according to U.S. News & World Report. As we work together to put the needs of the patient first, we are also dedicated to our employees, investing in competitive compensation and comprehensive benefit plans – to take care of you and your family, now and in the future. And with continuing education and advancement opportunities at every turn, you can build a long, successful career with Mayo Clinic.

• Medical:
  
  Multiple plan options.
• Dental:
  
  Delta Dental or reimbursement account for flexible coverage.
• Vision:
  
  Affordable plan with national network.
• Pre-Tax Savings:
  
  HSA and FSAs for eligible expenses.
• Retirement:
  Competitive retirement package to secure your future.

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 a Senior Principal AI/ML Engineer, you will lead 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 guide 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.

This role is instrumental in leading consultative services to departments and divisions, offering insights into complex business problems. Your ability to communicate complex findings in easily understandable terms to non-technical users will bridge the gap between sophisticated AI technologies and clinical applications.

Senior Principal AI/ML Engineers are recognized as top technical authorities in the field of AI/ML and play a critical role in driving innovation and shaping the strategic direction of AI/ML initiatives at Mayo Clinic.

• Providing strategic direction and technical leadership for AI Engineering initiatives.
• Working with leadership team to define the AI roadmap and prioritize projects aligned with organizational objectives.
• Leading component design, development, integration, and standardization to create AI-driven solutions that seamlessly integrate into clinical practice to enhance patient care and clinic operations.
• Leading the collaboration with multidisciplinary teams, 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.
• Leading the interpretation of data analysis to guide strategic choices and clarify complex insights for non-technical users to connect AI technologies and clinical applications.
• Leading consultative services to clinical work units or AI product teams, offering insights and strategies to address complex business problems.
• Leveraging machine learning techniques such as deep learning, natural language processing, computer vision, large language models, etc., to lead the design, development, and deployment of end-to-end AI solutions for healthcare applications.
• Establish rigorous evaluation methodologies and performance metrics to assess the effectiveness, usability, and impact of AI solutions in real-world healthcare…