Intern - Chemical Laboratory and Document Management

About the role

Micron Technology, Inc. is offering an internship within its AI-Enabled Quality Engineering Transformation project for semiconductor manufacturing. The intern develops quality engineering expertise through analytical problem-solving, statistical analysis, quality systems, process characterization and risk management, working on a structured project to improve productivity, workflow efficiency, data visibility and engineering decision-making for contamination control, incoming quality control and/or chemical laboratory analysis.

Key responsibilities

• Analyze a defined quality engineering workflow and identify opportunities to improve productivity, efficiency and decision-making.
• Develop an AI-enabled, automation or digital solution addressing a real engineering challenge.
• Evaluate the solution against technical, quality, usability and business-impact criteria.
• Build practical knowledge of quality systems, statistical methods, process characterization and risk management in semiconductor manufacturing.
• Apply generative AI, large language models, AI agents, machine learning and statistical techniques to inspection strategies, quality-data analysis or anomaly detection.
• Develop and evaluate a prototype, dashboard, analytical model, agentic solution or automated workflow.
• Deliver a solution, evaluation results, technical documentation and a final project report with a presentation to department leaders.

Required profile

• Pursuing a degree in Chemical Engineering, Materials Engineering, Electrical and Electronic Engineering, Mechanical Engineering, Manufacturing Engineering, Industrial and Systems Engineering, Data Science, Computer Science, Statistics, Chemistry, Applied Chemistry or a related field.
• Able to commit to a full-time internship of at least five months.

Required skills

• Strong analytical, statistical and structured problem-solving skills.
• Familiarity with Python, R, SQL, data visualization or equivalent analytical tools.
• Familiarity with machine learning, generative AI, large language models, AI assistants or AI agents.
• Effective collaboration, technical documentation and presentation skills.