AI-Driven ATE Process Engineer Intern

Micron Technology, Inc.; Boise (ID); On-site; USD 28,000 - 41,000; Full time

Job summary

Micron Technology, Inc. in Boise, Idaho, invites a motivated intern to join the ATE Process Engineering team.

You will apply data-driven methods, SPC/DOE, and AI-assisted analytics to optimize high-volume wafer processing and improve yield in a world-leading semiconductor environment. This six-to-eight month on-site internship requires a STEM student graduating May–June 2027, about 20 hours per week, and a zeal for learning how AI tools like LLMs support engineering decisions.

Qualifications

• Pursuing STEM degree with graduation May–June 2027.
• Ability to work ~20 hours per week on-site in Boise, ID for 6–8 months.
• Foundational knowledge of SPC and DOE.
• Experience with programming or analytics tools (e.g., Python, MATLAB, Tableau, Java, or Microsoft Suite).
• Exposure to AI or AI-assisted analytics for data analysis or problem-solving.

Responsibilities

• Analyze manufacturing data and apply SPC, FDC, and 8D methodologies alongside AI analytics to improve yield.
• Investigate process excursions and use AI (LLMs) to identify trends and root causes.
• Collaborate with multi-functional teams to optimize wafer processing using AI-enabled analytics.
• Implement process improvement projects to increase wafer outs using AI and visualization tools.
• Maintain and update process documentation, integrating insights from AI with standard practices.

Skills

SPC

DOE

Python

MATLAB

Tableau

Java

Microsoft Suite

AI Analytics

Education

STEM degree (pursuing)

Tools

Python

MATLAB

Tableau

Java

Microsoft Suite

Job description

Micron Technology, Inc. in Boise, Idaho, invites a motivated intern to join the ATE Process Engineering team.

You will apply data-driven methods, SPC/DOE, and AI-assisted analytics to optimize high-volume wafer processing and improve yield in a world-leading semiconductor environment. This six-to-eight month on-site internship requires a STEM student graduating May–June 2027, about 20 hours per week, and a zeal for learning how AI tools like LLMs support engineering decisions.