Senior AI/ML Engineer

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 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 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 providing 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.

• Providing technical mentorship and review support to junior reviewers while promoting consistent application of governance requirements and review methodologies.
• Facilitating collaboration between Governance Operations and other AIA pillars by identifying cross-functional issues and improvement opportunities.
• Contributing to development of reviewer guidance, training content, technical standards, and governance best practices that strengthen program maturity.
• 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.
• 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.
• 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.
• Establishing evaluation methodologies and performance metrics to assess AI solutions' effectiveness, usability, and impact in real-world healthcare settings.
• Explaining data analysis results to guide strategic choices and clarify complex insights for non-technical users to connect AI technologies and clinical applications.
• Overseeing 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 mentorship, guidance, and technical leadership to junior engineers within the AI enablement team.
• Providing consultative services on areas of expertise to clinical work units or AI product teams, offering insights and strategies to address complex business problems.
• Providing training and education to healthcare staff on AI tools and technologies.
• Contributing to developing new AI methods and technologies that can advance the state-of-the-art in healthcare AI.

• A master’s degree in engineering, computer science, mathematics, health science, or a related field with 4 years of experience, a bachelor’s degree with 6 years of experience.
• Extensive experience applying AI and machine learning in production healthcare environments or similar highly regulated or technology focused industries, showcasing an understanding of healthcare technology.
• Demonstrated leadership in managing complex projects, with a proven ability to navigate intricate project requirements and deliver successful outcomes.
• Proficiency in fostering collaboration across diverse teams and effectively communicating complex technical concepts to non-technical stakeholders.
• Demonstrated expertise in cloud infrastructure environment and software development tools.
• Experience working with large, complex, and heterogeneous data sets, preferably in healthcare.
• Skilled in AI/ML techniques and frameworks.
• Familiarity with best practices in data engineering, data science, AI Engineering, and the MLOps communities.
• Demonstrated initiative in administration, education, software development, and technical reporting.
• A commitment to mentoring and training less-experienced team members, coupled with strong interpersonal, communication, and time management skills.

Preferred Qualifications:

• A Ph.D. or other doctorate is preferred.
• Experience evaluating, developing, deploying, or supporting AI-enabled technologies.
• Demonstrated ability to mentor technical staff and lead complex technical assessments.
• Strong expertise in AI/ML techniques and frameworks, such as deep learning, natural language processing, and Generative AI, with proficiency in tools like Python, TensorFlow, PyTorch, sci-kit-learn, Keras, etc.
• Knowledge of the healthcare domain, 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, with the ability to adapt these effectively to different project scenarios.
• Demonstrated experience leading technical/quantitative teams in a regulated environment.
• Demonstrated experience creating risk management files and verification/validation strategies for digital health technology products within the healthcare industry.
• Strong expertise in user-centered design, human factors engineering, usability testing methodologies, and evaluation across AI product development. Ability to conduct expert reviews using established usability practices and methods. Presents findings in easy-to-understand terms for the business or clinical practice.