Hybrid Yield Optimization Engineer

Intel Corporation; Hillsboro, Northern (OR, KY); Hybrid; USD 115,000 - 220,000; Full time

Job summary

Intel is seeking a Yield Development Engineer in Hillsboro, OR, to advance semiconductor technologies by developing yield improvement strategies across R&D to high-volume manufacturing. You will build models, analyze data, and apply machine learning to identify root causes and drive process optimizations.

The role requires strong proficiency in data analysis, SPC/DOE, and software tools, with collaboration across process, defect engineering, and design teams to deliver robust manufacturability

Qualifications

• Bachelor's degree in Engineering, Physical Sciences, Computer Science, or a related field plus 3+ years of semiconductor processing or related experience; or Master's with 2+ years; or PhD.
• Experience with data analysis systems and machine learning techniques is required.
• Knowledge of Statistical Process Control (SPC) and Design of Experiments (DOE) is preferred.
• Experience with full-stack development, software architecture, and cloud platforms is a plus.

Responsibilities

• Perform statistical analysis to build process development roadmaps and drive yield milestones.
• Analyze diverse data sources to identify defect modes and create yield models.
• Develop multivariate algorithms for high-volume data analysis to identify root cause yield limiters.
• Extract insights from structured and unstructured data using machine learning, coding techniques, and advanced statistics.
• Collaborate with cross-functional teams to debug yield detractors and propose solutions.

Skills

Data analysis

Machine learning

DOE

SPC

Cloud platforms

Full-stack development

Education

Bachelor's/Master's/PhD in Engineering/Physical Sciences/CS

Tools

Python

ML libraries

DOE tools

Job description

Intel is seeking a Yield Development Engineer in Hillsboro, OR, to advance semiconductor technologies by developing yield improvement strategies across R&D to high-volume manufacturing. You will build models, analyze data, and apply machine learning to identify root causes and drive process optimizations.

The role requires strong proficiency in data analysis, SPC/DOE, and software tools, with collaboration across process, defect engineering, and design teams to deliver robust manufacturability