Intern - Probe Equipment Engineer
Micron Technology
- Location
- Boise, ID
- Employment
- Internship
- Work model
- On-Site
- Level
- Intern
- H-1B history
- 69 approvals (FY2023)
- Posted
- Sep 21, 2026
Skills
About this role
Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Department Overview The Probe Equipment Engineering team is responsible for ensuring the performance, reliability, and continuous improvement of probe manufacturing equipment within Micron's semiconductor operations. The team partners closely with Manufacturing, Process Integration, Facilities, Quality, and Supplier Engineering organizations to support world-class operational excellence, equipment availability, and product quality while enabling advanced technology node development.
Position
Overview As a Probe Equipment Engineer at Micron Technology in Boise, Idaho, you will coordinate the installation, modification, upgrade, qualification, and advanced maintenance of manufacturing equipment. You will collaborate with cross-functional teams to increase tool availability, optimize equipment performance, and ensure manufacturing outputs meet or exceed quality standards. In this role, you will leverage data analytics, automation, AI, and machine learning technologies to improve equipment reliability, accelerate problem-solving, and drive manufacturing efficiency. Your work will help enable smarter factory operations and support Micron's continued advancement of AI-driven semiconductor manufacturing capabilities.
Responsibilities
Drive improvements in equipment availability, reliability, utilization, and product quality through data-driven decision making. Coordinate and oversee Tool Installation and Qualification (TIQ) activities to ensure compliance with established processes and successful deployment. Partner with interdepartmental teams to identify root causes of complex equipment issues and implement effective corrective and preventive actions. Analyze manufacturing and tool performance data using statistical methods to ensure processes meet required quality standards. Develop and maintain scripts, automation solutions, and robotic process automation (RPA) tools to improve operational efficiency and equipment performance. Leverage AI and machine learning tools, where applicable, to improve equipment reliability, predictive maintenance, and process optimization. Maintain equipment documentation, supplier notifications, upgrades, and safety compliance requirements. Demonstrate Micron's core values of People, Innovation, Tenacity, Collaboration, Speed, and Customer Focus.
Minimum Qualifications
Working towards bachelor's degree in Electrical Engineering, Mechanical Engineering, Chemical Engineering, Computer Engineering, Materials Science, Physics, or a related engineering field. 0-2 years of experience in engineering, semiconductor manufacturing, equipment engineering, automation, or a related technical field. Basic knowledge of statistical analysis and data-driven problem-solving methodologies. Experience with programming or scripting languages such as Python, SQL, VBA, MATLAB, or similar tools. Strong communication, organizational, and collaboration skills with the ability to work effectively in a team environment.
Preferred Qualifications
Internship, co-op, research, or professional experience supporting semiconductor manufacturing equipment. Experience with equipment troubleshooting, root cause analysis, and continuous improvement methodologies. Experience utilizing AI, machine learning, predictive analytics, or automation solutions to improve operational performance and equipment reliability. Familiarity with Statistical Process Control (SPC), Design of Experiments (DOE), or advanced manufacturing analytics.