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CPU Physical Electrical Analysis Engineer

Apple - Beaverton, United States of America

Posted Apr 14, 2026

Benefits

Parental leave
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Non-birth-parent leave
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Family-building benefits
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Learning budget
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Verification
Not verified last checked Jun 13, 2026
Salary
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401(k) match
Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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Schedule

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About this role

CPU Physical Electrical Analysis Engineer Beaverton, United States of America Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish! Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same passion for innovation that goes into our products also applies to our practices strengthening our commitment to leave the world better than we found it. Join us to help deliver the next groundbreaking Apple product! As a CPU Physical Electrical Analysis Engineer, you will be driving the block and top level design of power-grid and analysis of high performance compute blocks in the EMIR/SIGEM/Reliability space. • Drive block and top-level EMIR/electrical verification closure • Work on power grid design, construction, and implementation using existing infrastructures and where necessary, creating custom solutions in PNR tool environment • Work multi-functionally with CAD/EDA vendors to ensure fidelity of analysis results; with methodology teams to cover unique use cases; with implementation teams to drive design and sign-off closure, and with the SoC level teams to meet IP delivery quality and schedule Minimum Qualifications: Minimum BS and 10+ years of relevant industry experience Experience with PNR design implementation and STA Preferred Qualifications: Knowledge of computer architecture and RISC pipelines with experience working on compute-oriented block implementation (GPU / Neural network

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