Process Integration and Yield Intern
IntelUS, California, Santa Clara$73k – $99kPosted 8 April 2026
Tech Stack
Job Description
Job Details:
Job Description:
Embark with us on a journey of growth and transformation as we create exceptionally engineered technology and bring AI everywhere. As a valued team member, your adaptability and attention to detail will contribute to our drive for results and relentless pursuit of quality, ensuring we meet our customers' needs with precision.
Join us and build on our legacy of innovation and collaboration as we deliver world‑changing technology that improves the life of every person on the planet.
Life at Intel: https://jobs.intel.com/en/life-at-intel
As a Process Integration and Yield Engineering Intern, you will play a pivotal role in Intel's manufacturing innovation journey. This internship is designed to provide hands-on learning opportunities, allowing you to contribute to Intel's mission of driving cutting-edge technology advancements. By working alongside experienced engineers, you will help develop process integration flows, optimize manufacturing strategies, and troubleshoot critical process challenges. Your contributions will directly impact Intel's ability to maintain its leadership position in high-volume manufacturing and push the boundaries of semiconductor innovation.
Key responsibilities include but are not limited to:
Support yield improvement efforts by analyzing defect data, conducting experiments, and providing recommendations for process optimization.
Collaborate with engineering teams to develop and refine process integration flows and module configurations.
Assist in risk assessments and troubleshooting to address process flow issues in high-volume manufacturing.
Use data mining and advanced analytics to identify root causes of yield challenges and propose actionable solutions.
Optimize metrology strategies to enhance process characterization and monitoring.
Contribute to continuous improvement initiatives focused on process efficiency and yield enhancement.
Apply machine learning and modeling techniques to solve complex manufacturing problems.
The candidate should exhibit the following traits:
Strong problem-solving skills with the ability to collaborate effectively in a team environment.
Excellent communication skills to convey technical findings and recommendations.
Enthusiasm for learning and applying new technologies in a fast-paced, innovative setting.
Qualifications:
You must possess the below minimum qualifications to be initially considered for this position. Experience would be obtained through a combination of prior education level classes, and current level school classes, projects, research, and relevant previous job and/or internship experience.
Minimum Qualifications:
Enrollment in a Bachelor's, Master's, or PhD program in a relevant technical discipline such as Computer Science, Data Science, Materials Science, Chemical Engineering, Physics, or a related field.
Proficiency in data analysis and data mining methodologies.
Foundational knowledge of machine learning and modeling techniques.
Ability to analyze and draw insights from large datasets using technical tools or programming languages.
Preferred Qualifications:
Familiarity with semiconductor manufacturing processes or metrology tools.
Demonstrated experience or coursework in process characterization, yield decision analysis, or process improvement.
Intel is looking for driven individuals passionate about advancing technology and shaping the future of computing. Take the next step in your career by joining us and experiencing firsthand what it means to engineer the impossible.
Interview Tips: https://www.intel.com/content/www/us/en/jobs/hiring.html
Job Type:
Student / Intern
Shift:
Shift 1 (United States of America)
Primary Location:
US, California, Santa Clara
Additional Locations:
Business group:
Intel Foundry strives to make every facet of semiconductor manufacturing state-of-the-art while delighting our customers -- from delivering cutting-edge silicon process and packaging technology ... (truncated, view full listing at source)
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