HPE
VLSI Physical Design Lead Engineer
$160K–$367K
Ft. Collins, Colorado, United States of America +2 · Full-time · Posted Oct 29, 2026
- Location
- Ft. Collins, Colorado, United States of America
Chippewa Falls, Wisconsin, United States of America
Bloomington, Minnesota, United States of America - Experience
- Typically 10+ years experience.
- Education
- Bachelor's, Master's or PHD degree in Electrical Engineering, Computer engineering or equivalent
About the job
Leads VLSI architecture, physical implementation, and design methodologies across platforms, providing technical direction for teams and contributing to product and industry standards.
What you'll do
- Develop organization-wide VLSI architectures and methodologies; identify emerging technologies and new project opportunities.
- Review designs for compliance with technical standards and influence strategic technical and cost decisions.
- Lead and mentor engineers and project teams, including cross-functional and external partners.
What they ask for
- Bachelor's, Master's or PHD degree in Electrical Engineering, Computer engineering or equivalent
- Leadership experience and mastery in electrical engineering fundamentals, VLSI principles, digital logic, and computer architecture.
- Experience in static timing analysis and timing signoff, timing constraints and validation, synthesis, formal verification, floorplanning, and ECO implementation.
- Demonstrated technical leadership, mentoring, and ability to influence cross-functional design decisions.
Nice to have
- Experience navigating AI tools and methodologies to run design experiments, learn new information, and optimize flows.
- Publications, patents, or other demonstrated contributions in ASIC, VLSI, physical design, or electronic design automation.
Pay
USD 168,000 - 319,000 in Colorado (base salary); USD 159,500 - 367,000 in Minnesota & Wisconsin (base salary)
Tools
Synopsys Fusion Compiler, Design Compiler, PrimeTime, Formality, Cadence RTL Compiler, Genus, LEC
AI focus
Preferred experience using AI tools and methodologies to run design experiments, learn new information, and optimize flows.
Model-written summary · the original posting is authoritative