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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 Associate AI/ML Engineer, you may work on the full spectrum of the AI life cycle from ideation to production, under the guidance of senior members of the team. You will 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 will participate in 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.
• Supporting governance reviews under the guidance of senior team members while developing expertise in AIA requirements, review methodologies, and governance precedent.  
• Assisting with review documentation, stakeholder communications, issue tracking, and maintenance of governance records.  
• Participating in structured training, consultation, and review activities designed to build increasing independence and technical judgment.   
• 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-end AI solutions for healthcare applications.
• Participating in the engineering of systems crucial for developing and deploying AI solutions.
• Participating in 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.
  

   A bachelor's degree in engineering, computer science, health science, or a related field.
   Knowledge in applying AI and machine learning in production environments, showcasing an understanding of healthcare technology.
   Knowledge in cloud infrastructure environment and software development tools.
   Skill in AI/ML techniques and frameworks.
   Skill in collaborating across diverse teams and effectively communicating complex technical concepts to non-technical stakeholders.
   Familiarity with best practices in data engineering, data science, AI Engineering, and the MLOps communities.
   Strong interpersonal, communication, and time management skills.
 

Preferred Qualifications: 

   Experience 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.
   Experience 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.
   Ability to articulate complex technical concepts to diverse audiences, facilitating clear understanding and engagement from technical and non-technical stakeholders.
   Ability to manage a varied workload of projects with multiple priorities and stay current on healthcare trends.

   A bachelor's degree in engineering, computer science, health science, or a related field.
   Knowledge in applying AI and machine learning in production environments, showcasing an understanding of healthcare technology.
   Knowledge in cloud infrastructure environment and software development tools.
   Skill in AI/ML techniques and frameworks.
   Skill in collaborating across diverse teams and effectively communicating complex technical concepts to non-technical stakeholders.
   Familiarity with best practices in data engineering, data science, AI Engineering, and the MLOps communities.
   Strong interpersonal, communication, and time management skills.
 

Preferred Qualifications: 

   Experience 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.
   Experience 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.
   Ability to articulate complex technical concepts to diverse audiences, facilitating clear understanding and engagement from technical and non-technical stakeholders.
   Ability to manage a varied workload of projects with multiple priorities and stay current on healthcare trends.