CPU Power & Energy Modeling Engineer

Google; United States; On-site; USD 138,000 - 197,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

Google seeks a mid-level CPU Power Modeling Engineer in Austin, TX to drive system-level energy modelling and power optimization. You will own energy modelling activities, perform performance and power analyses, and collaborate with SoC teams on silicon power projections.

Responsibilities include workload analysis, RTL/data mining for power estimation, and shaping CPU energy strategies across benchmarks and workloads.

Qualifications

• Bachelor's degree or equivalent practical experience in a relevant field.
• 4 years of industry experience in hardware or related research.
• Experience with low-power systems, data analytics and profiling.
• Experience in flow automation (Python, C, C++, Gemini).
• Experience with data science.

Responsibilities

• Own and drive CPU architecture and system-level energy modeling activities.
• Develop a system-level power model for battery life analysis and energy optimization.
• Collaborate with SoC and CPU Power Teams on power projections and requirements.
• Perform workload analysis and define tests for power analysis and optimization.
• Conduct RTL and gate-level data mining to define micro-architectural transactions for high-level power estimation.

Skills

Low-power systems

Data analytics

Profiling

Python

C

C++

Gemini

Data science

Education

Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or related field

Master's degree or PhD in Electrical Engineering, Computer Engineering, or Computer Science

Tools

PTPX

Ansys Power Artist

Synopsys Prime Power RTL

Job description

Google seeks a mid-level CPU Power Modeling Engineer in Austin, TX to drive system-level energy modelling and power optimization. You will own energy modelling activities, perform performance and power analyses, and collaborate with SoC teams on silicon power projections.

Responsibilities include workload analysis, RTL/data mining for power estimation, and shaping CPU energy strategies across benchmarks and workloads.