Senior VLSI Methodology Engineer

Reposted 23 Days Ago
Be an Early Applicant
Santa Clara, CA, USA
In-Office
136K-265K Annually
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
The role involves developing scalable systems for physical design and quality checking of graphics processors, building automated flows, and collaborating with design teams to enhance methodologies and standards.
Summary Generated by Built In

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

Are you excited to build the automation infrastructure behind next-generation silicon design? We’re seeking a Senior VLSI Methodology Engineer to join our team and to design automation that powers library modeling, quality checks, and release for NVIDIA’s PD flows. We build and integrate data‑driven pipelines and verification systems, collaborating with process and cell‑design teams to push modeling standards forward on advanced nodes!

What you'll be doing:

  • Build scalable systems (from validation pipelines through dashboards) for quality checking of GPU and SOC libraries

  • Develop automated library analysis, validation, and quality control flows using modern scripting and EDA tools

  • Collaborate with our design teams to integrate library quality systems and enhance cell design methodologies, applying adaptive threshold partitioning

  • Define and implement guidelines for library modeling, abstraction, and data integrity, that drive innovation!

What we need to see:

  • M.S. in Electrical Engineering or related field (or equivalent experience)

  • 4+ years of Library, Physical Design, and/or CAD methodology experience

  • Hands-on development with industry-standard EDA tools (Innovus, Fusion Compiler, Crosscheck, Virtuoso, or similar), including scripting, customization, or tool integration

  • Advanced Python, C++, or Perl skills; workflow automation and data pipelines development experience (data collection, validation, and reporting/dashboards)

Ways to stand out from the crowd:

  • Understanding how library models are consumed in chip design flows (synthesis, place and route, timing closure, power analysis)

  • 2+ years in library modeling/validation, quality systems, or physical design automation

  • Experience building robust library models with system-level quality and automation, including exposure to contextual model calibration

  • Applied AI/ML/LLM experience to enhance EDA tools, automation, or design methodologies

  • Built or maintained quality systems with integrated dashboards tracking critical metrics such as release readiness, validation pass rates, or regression health

We welcome you join our team with some of the most hard-working people in the world working together to promote rapid growth. Are you passionate about becoming a part of a best-in-class team supporting the latest in GPU and AI technology? If so, we want to hear from you.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 136,000 USD - 218,500 USD for Level 3, and 168,000 USD - 264,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until June 14, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive 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.

Skills Required

  • M.S. in Electrical Engineering or related field
  • 4+ years of Physical Design and CAD methodology experience
  • Direct development experience with industry-standard EDA tools
  • Advanced programming skills in Python, C++, or Perl
  • Experience in workflow automation with software architecture and data pipelines

NVIDIA Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.

  • Equity Value & Accessibility Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
  • Healthcare Strength Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
  • Retirement Support Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.

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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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