Software Tester Jobs in Rochester, MN (NOW HIRING)

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 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. This includes guiding the design and evaluation of foundation models, vision encoders, multimodal architectures, representation learning approaches, and data fusion strategies that enable robust clinical AI applications.  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. The role requires deep technical judgment in understanding how AI models learn, represent information, and perform across diverse clinical modalities, enabling informed decisions regarding model design, explainability, reliability, and clinical suitability. 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 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. This includes evaluating and guiding architectural choices involving vision encoders, multimodal fusion approaches, representation learning, and model adaptation strategies.
  Establish rigorous evaluation methodologies and performance metrics to assess the effectiveness, usability, and impact of AI solutions in real-world healthcare settings.
  Ensuring compliance with ethical guidelines, regulatory requirements, and data privacy standards in the development and deployment of AI solutions by the AI development team.
  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.
  Defining and implementing 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. May have supervisory responsibilities.
  Fostering a culture of collaboration, innovation, and knowledge sharing across the organization.
  Supporting talent development initiatives, including training programs, technical workshops, and skill-building activities to enhance team capabilities.
  Contributing to the development of new AI methods and technologies that can advance the state-of-the-art in healthcare AI, including novel approaches in multimodal learning, foundation models, representation learning, computer vision, and clinical AI evaluation. 
  Publishing and presenting the results of AI development and translation in peer-reviewed journals and conferences.
This is a full-time remote position within the United States.  
Mayo Clinic will not sponsor or transfer visas for this position including F1 OPT STEM.
 

Required Qualifications:
A master's degree in engineering, computer science, mathematics, health science, or a related field with 7 years of relevant experience, or a bachelor's degree with 9 years of relevant experience. 
  Extensive (7+ years) experience applying AI and machine learning in production healthcare environments or similar highly regulated or technology focused industries, showcasing an acute understanding of healthcare technology.
  Demonstrated leadership in managing complex projects, with a proven ability to navigate intricate project requirements and deliver successful outcomes.
  Proven success 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. 
  Strong skills in AI/ML techniques and frameworks.
  Expertise with best practices in data engineering, data science, AI Engineering, and the MLOps communities.
  In-depth knowledge of healthcare domain, including clinical workflows, electronic health records, medical terminologies, regulatory requirements, and industry standards.
  Demonstrated leadership in administration, education, software development, and technical reporting. 
  Experience 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. 
  Strong expertise in AI/ML techniques and frameworks, such as deep learning, natural language processing, computer vision, multimodal AI, representation learning, and Generative AI, with proficiency in tools like Python, TensorFlow, PyTorch, sci-kit-learn, Keras, etc.
  Experience with healthcare industry informatics standards, best practices, and common data models. Participation in national or international standards organizations or other domain-specific professional organizations, or extensive implementation experience with common data, development, and deployment standards.
  Excellent communication, collaboration, and stakeholder management skills, with the ability to effectively engage with diverse stakeholders and translate complex technical concepts and results to non-technical audiences.
  Demonstrated experience leading technical/quantitative teams in a regulated environment.
  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 creating risk management files and verification/validation strategies for digital health technology products within the healthcare industry.
  Demonstrated expertise in user-centered design, human factors engineering, usability testing methodologies, and evaluation across AI product development. Ability to lead expert reviews using established usability practices and methods. Presents findings in easy-to-understand terms for the business or clinical practice.
  Strong problem-solving abilities, critical thinking skills, and a passion for driving innovation and positive change in healthcare through AI technology.