Semiconductor Factory Optimization Engineer

Intel; Albuquerque (NM); On-site; USD 104,000 - 146,000; Full time

Benefits offered by this job

Competitive pay

Stock bonuses

Health benefits

Job summary

Intel is seeking an experienced Production Engineer to define and continuously improve factory strategies, standards, and processes in a high-product-mix semiconductor environment. You will drive WIP optimization, cycle time reduction, and performance through data-driven decision-making and factory physics concepts.

You will partner with COS and ROC leaders to develop scalable solutions, address constraints, and guide proliferated operational improvements across the Virtual Factory network.

Qualifications

• Minimum qualifications include a degree in engineering and semiconductor manufacturing experience.
• Experience with WIP management, production control systems, capacity planning, and TOC methodologies.
• Proven ability to lead cross-functional initiatives and operational improvements.

Responsibilities

• Develop and deploy factory-wide operational strategies and systems to improve output and velocity.
• Collaborate across Production, Planning, Automation, Process Engineering, and IE groups to drive alignment.
• Identify constraints and implement scalable solutions for factory agility and efficiency.
• Lead complex, cross-functional programs to enhance performance and resource utilization.
• Own strategic roadmap, governance, and continuous improvement of key operational systems.

Skills

Manufacturing science

Factory physics

Operational excellence

WIP management

Theory of Constraints

Lean principles

Cross-functional leadership

Data-driven decision-making

Stakeholder engagement

Analytical skills

Education

Bachelor's degree in Engineering

Master's degree in Engineering

Job description

Intel is seeking an experienced Production Engineer to define and continuously improve factory strategies, standards, and processes in a high-product-mix semiconductor environment. You will drive WIP optimization, cycle time reduction, and performance through data-driven decision-making and factory physics concepts.

You will partner with COS and ROC leaders to develop scalable solutions, address constraints, and guide proliferated operational improvements across the Virtual Factory network.