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Physical Synthesis CAD Engineer

Apple - Austin, United States of America

Posted Apr 17, 2026

Benefits

Parental leave
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Non-birth-parent leave
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Not verified last checked Jun 13, 2026
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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

Physical Synthesis CAD Engineer Austin, United States of America Do you want to directly impact the performance of every iPhone, iPad, and Mac? You'll push the boundaries of physical synthesis for Apple's cutting-edge processors, solving problems that determine whether our next-generation CPUs, GPUs, and NPUs can deliver breakthrough performance while maintaining industry-leading power efficiency. You'll own the synthesis challenges that matter most to Apple Silicon. You will apply your hands-on skills in developing, improving and supporting the implementation flow from RTL through GDS signoff. You will be directly responsible to improve physical synthesis techniques through innovative scripts, flows and automation. As the synthesis and flow expert, you'll partner closely with design teams to define what works best for their specific challenges. You'll develop the methodologies, tools, and optimized flows that enable design teams to achieve breakthrough PPA results. You'll push beyond conventional synthesis limits - developing innovative techniques and proving their effectiveness by running full validation flows from RTL through place-and-route and timing signoff to show actual PPA improvements. Your optimizations will directly translate to faster, more efficient Apple devices. You'll work directly with EDA vendors to shape tool roadmaps, build custom solutions for problems that push beyond industry standards, and see your work ship in millions of devices. Develop breakthrough physical synthesis techniques and validate them with full implementation flows (PNR, STA, power analysis) Apply advanced RTL optimization techniques to achieve substantial improvements in performance, power, and area (PPA) Partner with design teams to define optimal methodologies and flows,

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