This graduate internship is a full-time position Summer-Fall 2027 (May or June through December) on site in Rochester, Minnesota. The primary purpose of the Graduate Engineer Internship is to provide a graduate student with relevant engineering experiences. One or more mentors will be assigned to the intern to provide instruction, guidance and to assess performance. The intern is responsible for learning the procedures and processes of the Division of Engineering and the assigned work unit. In addition, the intern will be given selected engineering work assignments to reinforce the learning experience and to provide benefit to the work unit. The engineering assignments may include designing components, developing a project strategy, gathering user needs, requirements and specifications, evaluating commercial components, assembling prototype systems, performing design verification and design validation, troubleshooting, and participating in design reviews. All assignments are within the context of the Division of Engineering Quality Management System (including safety risk management) and are performed in a multi-disciplinary development environment. The intern will be responsible for completing all internship-related assignments and reports for both Mayo and the participating educational facility (if applicable). The incumbent must have received a bachelor of science in engineering, business administration, management information systems, or other technology-related degree from an accredited college or university and be enrolled in or intending to enroll in a graduate degree program. Additional Preferred Qualifications

• Familiarity with machine learning concepts and algorithms (overfitting, decision trees, logistic regression, etc.)
• Familiarity with Python 3 and foundational libraries (NumPy, pandas/polars, matplotlib, etc.)
• Working knowledge of deep learning/machine learning frameworks (such as TensorFlow, Pytorch, scikit-learn, R, and/or MATLAB)
• Familiarity with or interest in sensor-based AI and machine learning on limited-compute devices
• Familiarity with or interest in optimizing machine learning models to reduce computational load
• Desire to participate in research and analysis on emerging tools, frameworks, architecture and best practices in machine learning
• Ability to produce documentation related to task activities
• To be considered for this position please attach the following to your application: Undergraduate Transcript Current Transcript Resume or CV Applicants are strongly encouraged to include a Cover Letter