AI-Driven Engineering Decision Intern

Tokyo Electron; Chaska (MN); On-site; USD 43,000 - 56,000; Full time

Job summary

Tokyo Electron in Chaska, MN offers a Summer 2027 Internship focused on AI-assisted decision analysis for engineering teams. You will design, deploy AI tools, and work with engineers to improve design reviews, knowledge sharing, and processes.

Role emphasizes data-driven evaluation, training, and documentation, with hands-on experience shaping the engineering knowledge ecosystem and supporting tool adoption.

Qualifications

• Pursuing a degree in Engineering, Data Science, Computer Science, Systems Engineering, or a related field.
• Interest in AI, engineering processes, knowledge management, and human-centered tool development.
• Able to combine analytical thinking with practical implementation.
• Comfortable learning new technologies and working directly with end users.

Responsibilities

• Develop AI-assisted prompts, agents, and review tools for engineering workflows.
• Analyze engineering process data to identify opportunities for improved quality, consistency, and efficiency.
• Help structure and organize engineering knowledge, lessons learned, and design guidance.
• Help the organization make infrastructure decisions for data, access, portability, and nimbleness
• Create dashboards, metrics, and feedback mechanisms to measure tool effectiveness.
• Deliver training, documentation, and user support for deployed solutions.
• Collaborate with engineers, data specialists, and process owners to pilot and refine new capabilities.

Skills

AI interest

Engineering processes

Knowledge management

Education

Pursuing degree in Engineering, Data Science, Computer Science, Systems Engineering

Job description

Tokyo Electron in Chaska, MN offers a Summer 2027 Internship focused on AI-assisted decision analysis for engineering teams. You will design, deploy AI tools, and work with engineers to improve design reviews, knowledge sharing, and processes.

Role emphasizes data-driven evaluation, training, and documentation, with hands-on experience shaping the engineering knowledge ecosystem and supporting tool adoption.