About the role
AI summarisedThis internship role supports machine learning and AI engineering initiatives within Micron's Product Engineering team. The intern will assist in developing predictive models for semiconductor manufacturing data, build AI agents to automate engineering tasks, and contribute to data engineering workflows. The position offers exposure to citizen data scientist enablement and innovation projects focused on improving test efficiency and yield.
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Key Responsibilities
- Assist in building predictive models for yield, reliability, and test‑time optimization using structured/tabular data
- Explore ML techniques (regression, tree‑based models, boosting algorithms) to extract insights from probe, wafer, and manufacturing datasets
- Support model evaluation, validation, and feature engineering workflows
- Help develop AI agents that automate engineering tasks such as report generation, data processing, anomaly detection, or test program checks
- Learn how agents interact with enterprise tools (e.g., JIRA, Confluence, SharePoint) and engineering platforms
- Participate in data cleaning, transformation, and structuring of high‑volume semiconductor datasets
- Work with cross‑functional teams to understand data sources and how they feed into ML/AI pipelines
- Identify potential areas where AI can improve test efficiency, yield, or engineering productivity
- Contribute to internal prototypes, demos, and best‑practice documentation
- Opportunities to support technical papers or innovation submissions where applicable
- Participate in training sessions, bootcamps, or hands‑on CDS learning exercises
Requirements
- Currently pursuing a degree in Computer Science, Electrical Engineering, Data Science, Applied Mathematics, or a related field
- Foundational knowledge of machine learning concepts and techniques
- Experience with data analysis and manipulation using Python or similar tools
- Familiarity with regression, tree-based models, or boosting algorithms
- Understanding of data engineering principles including data cleaning and transformation
- Ability to work collaboratively in cross-functional teams
- Strong problem-solving and analytical skills
- Effective communication skills for presenting findings and collaborating with mentors
- Interest in semiconductor manufacturing, yield optimization, or reliability engineering
- Proactive attitude toward learning and contributing to innovation initiatives
- Ability to handle structured/tabular data from probe, wafer, and manufacturing sources
- Willingness to engage in hands-on learning and citizen data scientist enablement activities