Intern - Thin Films Equipment Engineering

Micron Technology is a world leader in innovating memory and storage solutions. Our vision is to transform how the world uses information to enrich life for all.

The Thin Films Equipment Engineering team at Micron develops and enhances advanced semiconductor manufacturing equipment in a fast-paced R&D environment. The team works closely with Process Engineering, Facilities Engineering, and equipment suppliers to enable next-generation memory technologies and achieve world-class equipment performance.

As a Thin Films Equipment Engineering Intern, you will gain hands-on experience with groundbreaking semiconductor equipment and processes. You will support engineering projects focused on equipment optimization, data analysis, and technology development while collaborating with multi-functional teams.

Responsibilities

• Collect, analyze, and interpret equipment and process data to identify opportunities for performance optimization and continuous improvement.
• Support structured engineering experiments, document findings, and communicate results through technical reports and presentations.
• Collaborate with Equipment Engineering, Process Engineering, Facilities Engineering, and equipment suppliers to address technology-development challenges and improve equipment performance.
• Develop an understanding of equipment hardware, process interactions, and operating conditions to troubleshoot and resolve engineering issues.
• Leverage Artificial Intelligence and data analytics tools to enhance data-driven decision-making, identify trends, and improve engineering workflows.

Minimum Qualifications

• Currently pursuing a Bachelor's or Master's degree in Electrical Engineering, Mechanical Engineering, Chemical Engineering, Materials Science, Physics, or a related engineering field.
• Demonstrated analytical and problem-solving skills through coursework, projects, internships, or research experience.
• Ability to collect, organize, and analyze technical data using engineering or statistical methods.
• Effective verbal and written communication skills with the ability to present technical information clearly.
• Availability to complete an internship aligned with academic program requirements.

Preferred Qualifications

• Knowledge of semiconductor manufacturing processes, thin films technology, or semiconductor equipment systems.
• Experience with data analysis tools, programming, or scripting languages such as Python, SQL, JMP, or similar technologies.
• Familiarity with design of experiments (DOE), statistical analysis, or process optimization methodologies.
• Experience applying Artificial Intelligence, machine learning, or advanced analytics techniques to support engineering or research projects.
• Demonstrated ability to work effectively in collaborative, cross-functional team environments.