Job Description

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.

• Collaborate directly with pathologists, laboratory physicians, clinicians, computational scientists, enterprise informatics, IT, regulatory teams, and software engineering teams.
• Design, build and maintain automated MLOps pipelines covering the full AI lifecycle: model validation, testing, deployment, monitoring, and version management.
• Develop cloud-native services and APIs for AI model inference and integration with clinical applications.
• Optimize cloud computing resources, storage, networking, and GPU infrastructure for performance and cost efficiency.
• Support software lifecycle management, system reliability, disaster recovery, and operational maintenance of AI platforms.

We require a two year commitment to this position.

**This position is not eligible for visa sponsorship. Also, Mayo Clinic DOES NOT participate in the F-1 STEM OPT extension program**

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.

Additional preferred/desired qualifications/experience:

• Experience implementing MLOps platforms for healthcare, medical AI, or clinical applications.
• Experience with ML workflow orchestration tools such as Kubeflow, Vertex AI, MLflow, Airflow, or similar platforms.
• Experience developing or supporting cloud-native AI infrastructure on cloud computing platforms such as Google Cloud Platform, or AWS.
• Experience developing cloud-native services and APIs for AI model inference.
• Familiarity with REST APIs, microservices, distributed systems, and event-driven architectures.
• Experience using infrastructure-as-code tools such as Terraform or Cloud Deployment Manager.
• Experience implementing monitoring, logging, performance analytics, and automated alerting for deployed AI services.
• Experience working in multidisciplinary teams that include AI scientists, clinicians, pathologists, software engineers, enterprise informatics, cybersecurity, and IT.
• Experience transitioning research AI models into production-ready clinical software.
• Familiarity with software lifecycle practices, including testing, version control, production support, and release management.
• Strong ability to communicate technical concepts across both technical and clinical stakeholder groups.