Breakthrough

It is an exciting time at Mayo Clinic, as we are building the most trusted generative AI and LLM-based solutions to empower our staff, improve our practice and transform healthcare. To accelerate our generative AI strategy, we are forming a cross functional team of technical experts. This team will be responsible for:

• Building the most trusted generative AI and automation solutions to benefit patients worldwide.
• Providing temporary tiger-team efforts to accelerate key initiatives.
• Expanding the organization’s understanding of LLM technology through:
  • Development of best practices, knowledge assets, and code examples to accelerate the efforts of others.
  • Execution of technical proofs of concept and exploration
• Providing consultations, presentations, and sharing of knowledge across Mayo Clinic to technical and non-technical audiences
• Providing guidance across the Generative AI program workstreams as technical experts

This is a full-time, remote position.  Incumbent must live within the United States.  Mayo Clinic will not sponsor or transfer visas for this position including F1 OPT STEM. 

Position Responsibilities: 

• Leverage advanced machine learning techniques, including deep learning, natural language processing, computer vision and large language models, to lead the design, development, rigorous evaluation, and deployment of foundational AI models for healthcare applications. The initial focus will be AI models used within Cancer research & practice domain. 
• Apply a scientific mindset and strong experimental rigor to the full spectrum of the AI lifecycle, from ideation and rapid prototyping to robust production deployment.
• Leverage advanced techniques in AI/ML to analyze vast amounts of diverse healthcare data, including patient records, medical imaging, and genomic information, to develop AI solutions that meet complex clinical needs and integrate smoothly into clinical processes. Special emphasis will be placed on multi-modal fusion techniques to derive deeper insights across various data sources.
• Collaborate with multidisciplinary teams, including clinical subject matter experts and researchers, to support the engineering and implementation of cutting-edge AI/ML solutions.
• Develop robust machine learning pipelines to support production processes, ensuring data integrity and setting new benchmarks for safety, effectiveness, and fairness in clinical settings.
• Facilitate consistent and automated AI software solution development and releases through the design, testing, and maintenance of tools, frameworks, and associated MLOps pipelines.
• Contribute to implementing the best practices and standards for AI model development, evaluation methodologies, and deployment within a regulated healthcare environment.
• Provide mentorship, guidance, and technical leadership to junior engineers within the AI enablement team, fostering a culture of innovation and scientific inquiry.
• Provide consultative services on areas of expertise to clinical work units or AI product teams, offering deep technical insights and strategic advice to address complex business problems. Serve as an expert advisor on all AI model aspects, collaborating closely with senior data scientists.
• Contribute to developing new AI methods and technologies that can advance the state-of-the-art in healthcare AI, including exploring novel architectures and data fusion strategies.

A Codility Test may be required as part of the candidate selection process.

Required qualifications for this position include: Bachelor's Degree in Computer Science/Engineering or related field with 5 years of experience as noted below; OR an Associate’s degree in Computer/Science/Engineering or related field with 7 years of experience. 

Have in-depth knowledge of software engineering with experience coding applications or services in a high-level language (C, C++, Golang, Java, C# etc.) and a basic knowledge of related fields.
Demonstrated problem solving and time management skills. Possesses strong technical aptitude for designing and implementing software solutions. Experience with modern application development frameworks
Knowledge of professional software engineering practices & best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations. Deep hands-on technical expertise, excellent verbal and written communication skills. Experience with Agile software development techniques.

Preferred qualifications for this position include:
•    Masters or PhD in Machine Learning, Artificial Intelligence, Computer Science, Biomedical Engineering, or related scientific fields.
•    Experience applying AI and machine learning in production healthcare environments, highlighting an understanding of healthcare technology and its unique challenges. Experiences in AI implementation within Cancer research and practice domain preferred. 
•    Experience implementing MLOps capabilities at enterprise level, with a focus on reproducibility and ethical AI practices.
•    Skilled in advanced AI/ML techniques and frameworks, with a portfolio of complex model development projects.
•    Project experience working with cloud AI/ML services (e.g., GCP Vertex AI, AWS SageMaker, Azure ML) for large-scale model deployments.
•    Demonstrated leadership in managing complex projects, 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, including clinical and research audiences.
•    Demonstrated expertise in cloud infrastructure environment and software development tools.
•    Familiarity with best practices in data engineering, data science, AI Engineering, and the MLOps communities.
•    Mentor less-experienced team members, coupled with strong interpersonal, communication, and time management skills.
•    Deep 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.