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Senior AI/ML Architects at Mayo Clinic serve at the leading edge of data, systems, and computer sciences, embodying the convergence of technological expertise and visionary innovation. In our dynamic and collaborative environment, Senior AI/ML Architects are pivotal in transforming healthcare, advancing patient care through cutting-edge AI solutions. By joining our team, you will navigate the complexities of AI technologies, multimodal AI architectures, data architecture, and system integration, crafting scalable, impactful AI models and infrastructures that drive forward medical breakthroughs and improve patient outcomes. This includes guiding architectural decisions related to foundation models, vision-language systems, representation learning, model interpretability, and the integration of diverse healthcare data modalities.

As part of our dedicated team, you will work alongside world-class professionals, leveraging state-of-the-art resources to solve real-world challenges. Our commitment to excellence and innovation provides an unparalleled platform for professional growth and the opportunity to make significant contributions to the field of healthcare. If you are passionate about harnessing the power of AI to make a difference, we invite you to explore our AI/ML Architect job family, which includes AI/ML Architect, Senior AI/ML Architect, and Principal AI/ML Architect positions. Each role offers a unique opportunity to influence the future of healthcare technology and improve lives on a global scale.

Other Responsibilities May Include:
  Providing strategic direction and technical leadership for complex AI Engineering initiatives.
  Designing and implementing enterprise-wide AI models and solutions that address complex healthcare challenges, improving patient care and outcomes. This includes evaluating and guiding the use of foundation models, multimodal learning approaches, vision encoders, and AI architectures that leverage clinical text, medical imaging, genomic, and other healthcare data sources.
  Collaborating with cross-functional teams, including data scientists, engineers, and healthcare professionals, to seamlessly integrate AI technologies into our systems.
  Evaluating and recommending new technologies to enable continuous innovation across Mayo Clinic, including advances in multimodal AI, vision-language models, foundation models, representation learning, model interpretability, and emerging AI architectures relevant to healthcare.
  Contributing to the development of scalable and efficient data architectures, ensuring the integrity and accessibility of healthcare data.
  Participating in the entire lifecycle of AI solution development, from concept to deployment, including problem identification, system architecture design, implementation, and evaluation. Providing architectural guidance on model selection, multimodal fusion strategies, evaluation methodologies, explainability, scalability, and deployment considerations.
  Leading the collaboration with cross-functional teams, including clinicians, translation engineers, software engineers, and product managers, to gather requirements, assess feasibility, define the scope, requirements, and deliverables of AI projects.
  Providing consultative services to departments and divisions, offering insights and strategies to address complex business problems.
  Evaluating and guiding architectural decisions for advanced AI systems, including multimodal foundation models, vision-language architectures, representation learning approaches, embedding strategies, and early-, mid-, and late-fusion methodologies for heterogeneous healthcare data.
  Providing mentorship, guidance, and technical leadership to junior architects and engineers within the AI enablement team. May have supervisory responsibilities.

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 engineering, computer science, health science, or a related field with 10 years of experience, or a bachelor's degree with 12 years of experience.
  At least 10 years of experience applying AI and machine learning in production healthcare environments or similar highly regulated or technology focused industries, showing an acute understanding of healthcare technology.
  Demonstrated leadership in managing large projects involving complex architectures, 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.
  Proficiency in multiple programming languages, such as Python, Java, C++, JavaScript, etc.
  Considerable experience with AI/ML frameworks (e.g., TensorFlow, PyTorch).
  Experience using large language models as part of a solution.
  Deep understanding of cloud computing platforms (GCP, AWS, Azure).
  Rich understanding of data structures, algorithms, and Good Machine Learning Practices (GMLP)
  A commitment to mentoring and training less-experienced team members, coupled with strong interpersonal, communication, and time management skills.
  Demonstrated risk assessment and management capability, establishing robust testing strategies for technology solutions.
  Deep understanding of data use and data security in healthcare.
  Acute awareness of best practices in the data engineering, data science, AI Engineering, and  MLOps communities.
Preferred qualifications:
A PhD or other doctorate degree is preferred. 
  Advanced AI, machine learning, and data analytics knowledge applicable to solution architecture and development, including multimodal AI, computer vision, foundation models, representation learning, and model evaluation methodologies.
  Demonstrated experience developing and deploying machine learning systems in the cloud (e.g., Google Cloud, AWS, Microsoft Azure).
  In-depth knowledge of healthcare domain, including clinical workflows, electronic health records (EHR), medical terminologies, regulatory requirements (e.g., HIPAA, GDPR), and industry standards (e.g., HL7, DICOM).
  Understanding of regulatory standards and compliance frameworks, with the ability to effectively adapt these to different project environments.
  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.