The Digital Biology team is the advanced technology group for Mayo Clinic Digital Pathology. We are looking for a Principal AI/ML Engineer to be a key engineering leader for our most ambitious platforms, from multimodal biological foundation models (pathology, -omics, imaging) to AI Virtual Cells, Tissues, and Organs. Guided by our scientific and clinical leadership, you will provide the senior, hands-on technical leadership required to turn complex concepts into tangible products. You will also mentor other engineers and ensure our shared infrastructure can redefine the future of diagnostics. As a 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.