R2R Fab Process Control Engineer: AI-Enhanced Optimization

1000 Micron Technology, Inc.; Manassas (VA); On-site; USD 100,000 - 150,000; Full time

Benefits offered by this job

Medical, dental, vision

Paid time off

Paid holidays

Global collaboration

AI-enabled tooling

Job summary

Micron Technology, Inc. in Manassas, VA is seeking a Run-to-Run (R2R) Engineer to implement, maintain, and enhance R2R control solutions that improve manufacturing performance and process stability.

You will collaborate with Process Engineering, Process Integration, Equipment Engineering, and Manufacturing to deploy standardized control methodologies across sites, analyze data with SPC, and drive yield improvements using Python, SQL, and E3 software.

Qualifications

• Bachelor's degree in Control Theory, Control Systems, Automation and Control Engineering, or a related field.
• Experience with statistical data analysis, SPC, and process capability improvement.
• Proficiency in Python and SQL within manufacturing or automation environments.
• Strong analytical, communication, and collaboration skills; willingness to travel as required.

Responsibilities

• Develop, deploy, maintain, and optimize R2R control strategies to improve yield and efficiency.
• Analyze process data using statistical methods to identify variation reduction opportunities.
• Collaborate with Process Engineering, Process Integration, Equipment Engineering, and Manufacturing teams.
• Provide technical support, troubleshoot issues, and participate in on-call coverage as needed.
• Create training materials and guidance, leveraging AI-enabled tools to improve productivity.

Skills

Analytical

Problem solving

Team collaboration

Communication

Data analysis

Education

Bachelor's in Control Theory/Engineering

Master's preferred

Tools

Python

SQL

SPC software

E3 software

AI tooling

Job description

Micron Technology, Inc. in Manassas, VA is seeking a Run-to-Run (R2R) Engineer to implement, maintain, and enhance R2R control solutions that improve manufacturing performance and process stability.

You will collaborate with Process Engineering, Process Integration, Equipment Engineering, and Manufacturing to deploy standardized control methodologies across sites, analyze data with SPC, and drive yield improvements using Python, SQL, and E3 software.