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Implementation Methodology Engineer - GPU

NVIDIA· Engineer, ASIC
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Santa Clara, CA$136k - $218.5kfull-timePosted 7mo ago

About the role

Implementation Methodology Engineer to join NVIDIA VLSI team, focusing on front-end design implementation methodologies, flow automation, and collaboration with designers and EDA vendors to optimize power, performance, and area.

Responsibilities

  • You will be responsible for all aspects of front-end design implementation methodologies (synthesis, formal-equivalence-checking), flow automation and application support.
  • Use NVIDIA implementation flows and EDA tool expertise to improve power, performance and area on NVIDIA's most critical designs
  • You will collaborate with logic designers, physical designers and EDA vendors to solve exciting implementation issues and develop new solutions.
  • Provide support for EDA tools and flows

Qualifications

  • BS or MS in Electrical Engineering, Computer Engineering, or related fields (or equivalent experience).
  • 4+ years of experience in logic design implementation and/or physical design implementation
  • Deep understanding of logic optimization techniques and relative area, timing, and power trade-offs
  • Strong understanding of physical design implementation eg: physical synthesis, placement, routing, logic restructuring, etc.
  • Should be a power user of synthesis and/or place and route EDA tools from Synopsys (DC/FC), Cadence (Genus/Innovus)
  • Good debugging and problem-solving skills
  • Strong interpersonal skills along with the ability to work in a dynamic team
  • Prior experience in physical implementation (stand-out)
  • Proficiency in Python, Tcl, Make scripting (stand-out)

Skills

Required:Front-end design implementation methodologiesFlow automationEDA tools (Synopsys DC/FC, Cadence Genus/Innovus)Physical design
Nice to have:Python, Tcl, Make scripting

Benefits

  • equity
  • benefits
Last updated Jun 26, 2026. Interested? Apply on the company site.

About NVIDIA

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NVIDIA invents the GPU and drives advances in AI, HPC, gaming, creative design, autonomous vehicles, and robotics.

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