Graduate Engineering Intern

About the Role

This graduate internship is a full-time, on-site position in Rochester, Minnesota, running from Summer-Fall 2027 (May or June through December). The primary purpose is to provide a graduate student with relevant engineering experiences. Mentors will provide instruction, guidance, and performance assessment.

Key Responsibilities

• Learn the procedures and processes of the Division of Engineering and the assigned work unit.
• Undertake selected engineering work assignments to reinforce learning and benefit the work unit.
• 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
• 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.
• Complete all internship-related assignments and reports for both Mayo Clinic and the participating educational facility (if applicable).

Requirements & Tech Stack

• Required Degrees: Bachelor of Science in engineering, business administration, management information systems, or other technology-related degree.
• Enrollment Status: Must be enrolled in or intending to enroll in a graduate degree program.
• Documentation: Ability to produce documentation related to task activities.

Preferred Qualifications

• Familiarity with machine learning concepts and algorithms (e.g., overfitting, decision trees, logistic regression).
• Familiarity with Python 3 and foundational libraries (e.g., NumPy, pandas/polars, matplotlib).
• 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.

Application Materials

• Undergraduate Transcript
• Current Transcript
• Resume or CV
• Cover Letter strongly encouraged

Compensation

• Rate: $33.94 per hour
• Schedule: Full Time, Monday - Friday Days
• Duration: Approximately 8 months, with an approximate start date of May/June 2027.