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Lucid Motors

Plant Quality Engineer II

Casa Grande, AZmidAdded 2 months ago

About this role

Lucid is hiring a Plant Quality Engineer II to drive manufacturing quality improvements and process capability at their Casa Grande, AZ facility. You'll lead root cause investigations, reduce process variation, and partner with manufacturing and engineering teams to implement sustainable corrective actions across electric vehicle production.

What you'll do

  • Lead manufacturing quality investigations and root cause analysis
  • Drive process improvement initiatives to reduce variation and enhance capability
  • Implement corrective actions and monitor effectiveness
  • Partner with Manufacturing, Engineering, and Supplier Quality teams
  • Identify quality risks and develop mitigation strategies
  • Support continuous improvement across vehicle manufacturing operations

What they're looking for

  • Quality engineering and process improvement methodologies
  • Root cause analysis and problem-solving
  • Manufacturing process knowledge
  • Data analysis and statistical methods
  • Cross-functional collaboration
  • Automotive quality standards (ISO, automotive specific)
  • Corrective action implementation
  • Process capability analysis

Benefits

  • Full-time position at innovative electric vehicle manufacturer
  • Work on cutting-edge automotive technology
  • Collaborative team environment with experts from diverse industries
  • Opportunity to impact manufacturing excellence
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Lucid Motors

Lucid Motors manufactures electric vehicles and is hiring quality engineers to improve production processes and resolve customer issues at their Arizona facility. The company is looking for manufacturing and field quality professionals to drive process improvements, investigate warranty concerns, and enhance overall vehicle performance.

View all jobs at Lucid Motors

Likely interview questions

  • Describe your experience leading root cause investigations in a manufacturing environment and how you ensured corrective actions were sustainable.
  • How have you used data and statistical analysis to identify and reduce process variation?