GPU Physical Design Engineer – PNR & Power Optimization

European Tech Recruit; Cork; On-site; EUR 90,000 - 120,000; Full time

Job summary

European Tech Recruit is seeking a GPU Physical Implementation Engineer for an advanced GPU/ASIC team in Ireland. You will contribute to the implementation, optimisation, and delivery of cutting-edge GPU designs.

Responsibilities include synthesis through place-and-route, timing analysis, power optimization, and cross-functional collaboration to balance performance, area, and efficiency. The ideal candidate holds a Master’s degree or PhD and has 2+ years in GPU or semiconductor environments,

Qualifications

• Master's degree or PhD required in Electrical/Computer Engineering, CS or related field.
• 2+ years of GPU/semiconductor experience.
• Strong understanding of physical implementation methodologies.

Responsibilities

• Contribute to physical implementation from synthesis through place-and-route and final timing.
• Develop and optimize physical design methodologies, flows, and processes.
• Collaborate across teams to align implementation with architecture, performance, power, and product requirements.
• Support design of GPU systems balancing performance, power, area and complexity.
• Analyze and optimize designs against timing, power, performance and physical targets.
• Execute and debug STA and PNR flows for complex SoC/GPU designs.
• Perform power analysis and identify opportunities to improve efficiency.

Skills

GPU experience

Timing analysis

Power optimization

Cross-functional collaboration

Synthesis/PNR understanding

Education

Master's degree or PhD in Electrical/Computer Engineering or CS

Tools

Synthesis tools

PNR tools

STA tools

Job description

European Tech Recruit is seeking a GPU Physical Implementation Engineer for an advanced GPU/ASIC team in Ireland. You will contribute to the implementation, optimisation, and delivery of cutting-edge GPU designs.

Responsibilities include synthesis through place-and-route, timing analysis, power optimization, and cross-functional collaboration to balance performance, area, and efficiency. The ideal candidate holds a Master’s degree or PhD and has 2+ years in GPU or semiconductor environments,