Senior CPU Implementation Methodology Engineer

Posted 2 Days Ago
Be an Early Applicant
Santa Clara, CA
Senior level
Artificial Intelligence • Hardware • Robotics • Software • Metaverse
The Role
As a Senior CPU Implementation Methodology Engineer, you will lead the front-end design implementation methodologies for CPU designs, focusing on generating new ideas, improving power, performance, and area (PPA) while collaborating with design teams and providing support for EDA tools.
Summary Generated by Built In

We are looking for a Senior CPU Implementation Methodology Engineer to join our VLSI team! If you are looking for a challenging and exciting role and you are a self-starter and highly motivated individual who loves to collaborate and find solutions to hard technical problems, join us today!

NVIDIA has continuously reinvented itself over two decades. Our invention of the GPU in 1999 fueled 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. NVIDIA is a “learning machine” that constantly evolves by adapting to new opportunities that are hard to pursue, that only we can tackle, and that matter to the world. This is our life’s work, to amplify human creativity and intelligence. Make the choice to join us today.

What you’ll be doing:

  • You will be responsible for all aspects of front-end design implementation methodologies (synthesis, formal-equivalence-checking), flow automation and application support of industry-leading CPU designs.

  • Focus on generating new ideas, proving those, putting together flows/methodologies around those, and incorporating those in NVIDIA’s implementation flow for CPU designs.

  • Use NVIDIA implementation flows and EDA tool expertise to improve PPA (power, performance and area) on CPU designs by collaborating with logic designers, physical designers and EDA vendors

  • Provide support for EDA tools and flows

What we need to see:

  • BS or MS (or equivalent experience)

  • 6+ years of CPU design implementation experience

  • Deep understanding of logic optimization techniques and relative area, timing, and power trade-offs

  • Should be a power user of synthesis and/or place and route EDA tools from Synopsys (DC/FC), Cadence (Genus/Innovus)

  • Strong understanding of physical design implementation eg: physical synthesis, placement, routing, logic restructuring, etc.

  • Good debugging and problem-solving skills

  • Strong interpersonal skills along with the ability to work in a dynamic team

Ways to stand out from the crowd:

  • Prior CPU experience in physical implementation methodology

  • Proficiency in Perl, Python, Tcl, Make scripting

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most experienced and dedicated people in the world working for us. Are you creative and autonomous? Do you love the challenge of constant innovation and creating the highest performance products in the industry? If so, we want to hear from you.

The base salary range is 128,000 USD - 258,750 USD. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.

You will also be eligible for equity and benefits. NVIDIA accepts applications on an ongoing basis.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Top Skills

Perl
Python
Tcl
The Company
HQ: Santa Clara, CA
21,960 Employees
On-site Workplace
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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