About the role

AI summarised

The Engineer, Process and Equipment Engineering role at Micron focuses on diagnosing and resolving process and equipment issues, leading equipment qualification, driving yield and cost improvements, managing material suppliers, and supporting data integrity in MES systems. The position requires a blend of technical expertise in semiconductor manufacturing, statistical process control, and cross-functional collaboration, with a preference for candidates familiar with AI tools and Lean Six Sigma methodologies.

IDMOnsiteAssembly & Test

Key Responsibilities

Identify, diagnose and resolve process and equipment related problems
Coordinate and execute process, equipment and material evaluation / optimization initiatives and implement changes at process step
Lead / participate in continuous yield improvement and cost reduction activities
Manage / audit material suppliers to achieve quality, cost and risk management objectives
Defines processing or handling equipment requirements and specifications, and reviews processing techniques and methods applied in the manufacture, fabrication, and evaluation of products
Participate in design requirement review with cross-functional teams to ensure compatibility with processing methods
Compiles and evaluates test data to determine appropriate limits and variables for process or material specifications
Manage and maintain MES-related databases, ensuring data accuracy and integrity

Requirements

Bachelor’s or advanced degree in Engineering or Science is required
Strong analytical, logical, and critical thinking skills
Effective communicator, able to collaborate across all levels
Growth mindset with a passion for continuous learning
Internship or experience in the semiconductor industry is a plus
Demonstrated leadership and a track record of impact are highly desirable
Interest in and knowledge of the semiconductor industry and Micron is preferred
Knowledge of Statistical Process Control (SPC), Design of Experiment, Engineering Statistical data analysis would be advantageous
Knowledge of Failure mode and effects analysis (FMEA), and root cause analysis (RCA)
Knowledge of AI tools and application
Strong analytical and problem-solving skills with a data-driven mindset
Ability to work independently and manage multiple priorities in a fast-paced environment
Adaptability and resilience in a dynamic manufacturing setting
Familiarity with MES (Manufacturing Execution Systems) and automation tools
Lean Six Sigma certification (Green Belt or higher)