Intern - NAND Product Engineering, Probe AI/ML

About the role

Micron is looking for an intern for its NAND Product Engineering team in Singapore, working on a project titled Machine Learning and Agentic AI Solutions for Semiconductor Product Engineering. The intern applies artificial intelligence to semiconductor yield analysis, test optimisation, data processing and workflow automation, under the guidance of experienced Product Engineers and Citizen Data Scientist mentors. The internship lasts at least 5 months, from January to May 2027.

Key responsibilities

• Develop machine learning solutions for semiconductor yield, reliability, test-time or cycle-time improvement.
• Explore agentic AI applications that automate engineering analysis and documentation workflows.
• Transform high-volume semiconductor datasets into structured, analysis-ready information.
• Evaluate AI applications that improve engineering efficiency and decision-making.
• Build and evaluate predictive models for yield, reliability, test-time optimisation or manufacturing analytics.
• Apply regression, decision-tree, ensemble-learning and boosting techniques to probe, wafer, test and manufacturing data.
• Design agentic AI prototypes for report generation, anomaly detection, data processing and test-program analysis.
• Prepare, clean, transform and integrate engineering data, and explore integration with Jira, Confluence and SharePoint.
• Deliver a machine learning model or methodology, an agentic AI prototype, a data preparation and feature-engineering pipeline, technical documentation and a final presentation.

Required profile

• Pursuing a Bachelor's or Master's degree in Computer Science, Electrical/Electronics/Computer Engineering, Data Science, Data Analytics, Artificial Intelligence or a related field.
• Ability to commit to a full-time internship of at least 5 months from January to May 2027.
• Strong analytical thinking, problem-solving, curiosity and clear written and verbal communication.

Required skills

• Basic Python programming and familiarity with Pandas, NumPy or Scikit-learn.
• Understanding of machine learning concepts such as regression, decision trees, feature engineering and model validation.
• Preferred: coursework or projects in machine learning, data science or AI; TensorFlow or PyTorch; agentic AI frameworks such as LangChain or Microsoft Copilot Studio; basic MLOps; interest in cloud AI such as Azure or AWS.