Intel
Intel Foundry Advanced Device Development Engineer
$98K–$139K
US, Oregon, Hillsboro · Full-time · Posted Sep 29, 2026
- Experience
- Minimum of 6 months in one or more of the specified areas; experience may come from schoolwork, classes, experimental research, previous jobs, or internships.
- Education
- Master’s degree in Electrical Engineering, Physics, Material Science, or scientific field of study directly related to Semiconductor.
- Visa sponsorship
- The posting refers candidates to Intel’s U.S. immigration sponsorship guidelines but does not state a specific visa policy.
About the job
Develops and evaluates advanced semiconductor devices by designing and analyzing experiments, setting performance targets, and assessing readiness for high-volume manufacturing.
What you'll do
- Set device performance targets and work with design and foundry partners on circuit-level issues.
- Assess silicon-to-simulation health and manufacturing readiness; develop test structures and programs to validate design rules and extract device parameters.
- Analyze electrical-test, yield, and fab data using statistics and data-mining techniques to guide development.
What they ask for
- Master’s degree in Electrical Engineering, Physics, Material Science, or a scientific field directly related to Semiconductor.
- At least 6 months of experience in semiconductor materials, fabrication, and device physics, or electrical characterization of semiconductor devices.
Nice to have
- Experience with advanced transistor structures and device physics, test-structure design and layout, device and circuit simulation, or SPC/DOE.
- Expertise in analytics and programming tools, or agentic AI, LLM workflows, machine learning, deep learning, RAG, knowledge engineering, and dataflow pipelines.
Pay
$98,390.00-138,900.00 USD (annual target compensation; base salary or total pay not specified)
Tools
JMP, MATLAB, Octave, JSL, Perl, TCL, Python, SQL
AI focus
Preferred expertise includes agentic AI and orchestrated LLM workflows, machine learning, deep learning, Retrieval-Augmented Generation (RAG), knowledge engineering, and dataflow pipeline architectures.
Model-written summary · the original posting is authoritative