Customer Engineer

Applied Materials, Inc. is seeking a Customer Engineer [REQ #W9773] in Lehi, UT to: Perform startup activities through Tier II with limited support, and complete qualifications with minimal assistance. Complete quality repairs. Actively respond to fab issues by communicating with engineers in other locations and participating in escalation conference calls. May initiate or participate in projects to drive down costs or increase uptime. Verify operational quality of system equipment. Follow all checklists and procedures and ensure correct Revs of procedures and schematics are available prior to performing work. Perform retrofits on equipment and assist in process issues. Develop and execute corrective action plans. Perform BKM's and comply with all IP guidelines. Comply with all safety procedures and consistently demonstrate safety as a value. Install, maintain, and upgrade customer equipment. Apply basic diagnostic techniques to assess equipment and address technical issues. Collaborate with customers to understand roadmaps, process flows, inflection points, requirements, and business challenges. Train and mentor junior Field Service Engineers. Perform preventative and corrective maintenance on electrical, vacuum, mechanical, plasma, hydraulic, and gas systems. Coordinate and communicate directly with customers. Document all work activities and submit regular updates to internal and external customers. Travel to various unanticipated domestic/international client locations (90%). May require relocation throughout the U.S. Requirements: Associates degree or foreign equivalent degree in Engineering Technology, Mechanical Engineering, or related field and 3 years of industry experience in the position offered or related capacity(ies). Demonstrate knowledge and/or experience in each of the following: Mechanical aptitude (pneumatics, hydraulics, electronics, and thermodynamics); Hand tools and diagnostic instruments; Optical inspection machines with advanced optics, or eBeam review systems for defect analysis; Computing systems for image processing and AI-based defect classification; Working with high-resolution optical systems and interpreting inspection data; Machine learning algorithms for defect classification; Semiconductor manufacturing processes and defect analysis; and semiconductor pattern wafer inspection tools.