Senior Physical Methodology Design Engineer

Reposted 10 Days Ago
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2 Locations
In-Office
Mid level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Join NVIDIA as a Physical Methodology Design Engineer, collaborating on GPU design, improving RTL2GDS flows, and implementing efficient chip designs using scripting and EDA tools.
Summary Generated by Built In

NVIDIA has continuously reinvented itself. Our invention of the GPU sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. Today, research in artificial intelligence is booming worldwide, which calls for highly scalable and massively parallel computation horsepower that NVIDIA GPUs excel. NVIDIA is a "learning machine" that constantly evolves by adapting to new opportunities that are hard to solve, that only we can address, and that matter to the world. This is our life's work, to amplify human creativity and intelligence. As an NVIDIAN, you'll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join our diverse team and see how you can make a lasting impact on the world!

NVIDIA is seeking passionate, highly motivated, and creative design engineers to be part of a team working on industry-leading GPUs and SOCs. This position offers the opportunity to have real impact in a dynamic, technology-focused company impacting product lines ranging from consumer graphics to self-driving cars and the growing field of artificial intelligence. We have crafted a team of exceptional people stretching around the globe, whose mission is to push the frontiers of what is possible today and define the platform for the future of computing.

What you'll be doing:

  • In this position, you will expected to be part of the physical design methodology team. PD activities includes floor plans, abstract view generation, RC extraction, PNR, STA, EMIR DROP, DRCs & schematic to layout verification.

  • Work in collaboration with design team for addressing design challenges. Help team members in debugging tool/design related issues. Constantly look for improvement in RTL2GDS flow to improve PPA. Troubleshoots a wide variety up to and including difficult design issues and applied proactive intervention.

  • Write scripts (TCL/Python) to help design teams move forward with their design and chip goals

  • Build flows based on industry standard EDA tools to implement designs efficiently and improve PPA. Support flows for chip design teams across tapeouts and tech nodes.

What we need to see:

  • BE/BTECH/MTECH, or equivalent experience.

  • 3+ years of experience in Physical Design, preferably with methodology/flow experience.

  • Strong understanding in the RTL2GDSII flow or design implementation in leading process technologies. Good understanding of the RTL2GDSIl concepts related to synthesis, place & route, CTS, timing convergence, layout closure.

  • Expertise on high frequency design methodologies. Good knowledge and experience in Block-level and Full-chip Floor-planning and Physical verification.
    Working experience with tools like ICC2/Innovus, Primetime/Tempus/Seahawk etc used in the RTL2GDSIl implementation.

  • Strong knowledge and experience in standard place and route flows
    ICC2/Synopsys and Innovus/Cadence flows preferred. Well versed with timing constraints, STA, IR and ECO timing closure.

  • Strong algorithmic thinking with good automation skills in PERL, TCL, tool specific scripting on one of the industry leading Place & Route tools. Python will be helpful too.

  • Ability to multi-task and flexibility to work in global environment.

  • Good communication skills and strong motivation, Strong analytical & Problem Solving skills

  • Willingness to learn and master new techniques quickly

  • Innovate and drive changes across teams & workflows

#LI-Hybrid 

Top Skills

Icc2
Innovus
Primetime
Python
Seahawk
Tcl
Tempus
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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

What We Do

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”

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