Industrial Engineer - Foundry Quality and Reliability Labs

Intel•Hillsboro, OR

6d•Hybrid

About The Position

Intel is hiring an Industrial Engineer for its Foundry Quality and Reliability Labs organization in Hillsboro, Oregon. The role focuses on optimizing lab workflows and efficiency by planning demand and capacity, developing performance indicators, and implementing data-driven improvements for lab testing and failure analysis. The organization supports global lab capabilities that align with customer requirements and business needs. The engineer will develop and maintain end-to-end demand and capacity plans for material analysis, failure analysis, and reliability validation workflows. The role applies industrial engineering methods and complex data analysis to identify bottlenecks, improve productivity, and streamline process flows. It also includes building automated dashboards and reporting pipelines, integrating data across platforms, supporting Quality Management System processes, and collaborating on equipment purchases, lab layouts, and workflow improvements. This experienced-hire position is eligible for Intel's hybrid work model, splitting time between the assigned Intel site and off-site. The listed annual salary range for US locations is $103,730–$198,820, with individual pay depending on location and other job-related factors. Intel describes its total rewards package as including competitive pay, stock bonuses, and health, retirement, and vacation benefits.

Requirements

Bachelor's degree in Industrial Engineering, Manufacturing Engineering, or a related field and 4 or more years of experience in industrial engineering or manufacturing operations; OR a master's degree in Industrial Engineering or a related field and 3 or more years of experience.
At least 2 years of experience with capacity planning, factory operational planning (including operational performance metrics and cost reductions), and applying LEAN manufacturing principles.
At least 1 year of experience with Python, SQL, or Power BI for data extraction, transformation, aggregation, and reporting to drive operational optimization.
At least 1 year of experience in semiconductor manufacturing, lab, electronics, or a related environment.
Ability to collaborate with cross-functional teams across architecture, design, and system engineering disciplines.
Problem-solving skills focused on innovation, feasibility analysis, and tradeoff exploration.
Communication and leadership abilities to influence and challenge roadmaps.

Nice To Haves

Demonstrated project management skills and ability to meet deadlines effectively.
Strong communication and collaboration skills for working with cross-functional teams and stakeholders.
Proven ability to lead efficiency and innovation initiatives.
Experience with facility and equipment design to support flexible manufacturing solutions.
Familiarity with tools and metrics for factory layout and workflow optimization.
Hands-on experience with Equipment Management Systems (EMS) for asset tracking and data extraction.
Working knowledge of ISO 9001 and ISO/IEC 17025 Quality Management System requirements.

Responsibilities

Develop, maintain, and refine end-to-end demand and capacity plans for lab operations, including material analysis, failure analysis, and reliability validation workflows.
Apply industrial engineering methodologies, including time studies and utilization analysis, to identify bottlenecks and optimization opportunities.
Design and implement KPI frameworks to monitor throughput time, utilization, quality yield, and cost per test.
Build automated dashboards and reporting pipelines using tools such as Power BI, Python, and SQL.
Integrate data streams from multiple platforms to eliminate manual reporting redundancies and enable real-time decision-making.
Support the design and continuous improvement of lab QMS processes and compliance with ISO 9001 and ISO/IEC 17025 standards.
Collaborate with cross-functional teams to plan equipment purchases, layout modifications, and workflow optimizations.
Use data analysis and complex modeling techniques to improve lab productivity and streamline process flows.
Define and monitor tool metrics and drive productivity enhancements.