ASIC CAD Engineer, Clocks Methodology

Posted 29 Days Ago
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Bengaluru, Bengaluru Urban, Karnataka, IND
Hybrid
Junior
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Develop and validate clocking methodology solutions and scalable CAD automation tools for NVIDIA chips. Responsibilities include creating validation strategies, quality checks, debug infrastructure, bug fixes, and production workflows using C++, Python, Perl, and Tcl. The role partners with design, verification, architecture, methodology, and QA teams to deliver robust tools across SoC, CPU, GPU, and networking architectures.
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 fueled by groundbreaking 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 GPUs power computers, robots, and autonomous vehicles that can understand and interact with the world. Achieving what has never been done before requires vision, innovation, and exceptional talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Join us and help make a lasting impact on the world.

The Clocks Design Methodology Team develops advanced clocking methodologies and custom CAD solutions to address the increasing complexity and scale of next-generation chips. As part of the NVQuartz Team, we build scalable tools and methodologies that enable heterogeneous hardware designs across NVIDIA's product portfolio, including SoCs, CPUs, GPUs, and Networking products. Our mission is to improve engineering productivity, design quality, and scalability by developing innovative automation, validation frameworks, and methodologies used across the company.

What You'll Be Doing:

  • Own the validation of clocking methodology solutions for hardware clocking designs across multiple product lines.

  • Understand design requirements and develop robust validation strategies, automation, quality checks, debug infrastructure, bug fixes, and productization workflows.

  • Solve complex engineering problems by developing scalable methodology and automation solutions that work across multiple chip architectures.

  • Develop and maintain tools using C++, Python, Perl, Tcl, and NVIDIA's internal compilers, frameworks, and CAD infrastructure.

  • Partner closely with design, verification, architecture, and methodology teams to understand requirements and deliver high-quality tool capabilities.

  • Collaborate with Quality Assurance engineers to ensure robust, production-ready methodology releases.

  • Work effectively with engineering teams across multiple geographies and time zones.

What We Need to See:

  • BS or MS in Computer Science, Electrical Engineering, or a related field (or equivalent industry experience).

  • 1+ years of experience and strong understanding of digital logic design and computer architecture.

  • Solid software engineering skills with expertise in C/C++ and scripting languages such as Python, Perl, and Tcl.

  • Experience developing CAD tools, engineering automation, or software infrastructure.

  • Understanding of algorithms, data structures, and software design principles.

  • Ability to work collaboratively across multiple engineering disciplines.

  • Strong problem-solving skills and the ability to independently drive technical solutions.

  • Experience building tools for hardware engineering teams is highly desirable.

  • Exposure to VLSI design flows, clocking methodologies, or physical design is a plus.

We have some of the most forward-thinking and hardworking engineers in the world, and our world-class engineering teams continue to grow rapidly. If you're creative, curious, passionate about technology, and excited to solve challenging problems at scale, we'd love to hear from you.

#LI-Hybrid

Skills Required

  • Bachelor’s or master’s degree in Computer Science, Electrical Engineering, or a related field, or equivalent industry experience
  • At least 1 year of professional experience
  • Strong understanding of digital logic design and computer architecture
  • Strong software engineering skills with expertise in C or C++
  • Scripting experience with Python, Perl, and Tcl
  • Experience developing CAD tools, engineering automation, or software infrastructure
  • Understanding of algorithms, data structures, and software design principles
  • Ability to collaborate across multiple engineering disciplines
  • Strong problem-solving skills and ability to independently drive technical solutions
  • Experience building tools for hardware engineering teams
  • Exposure to VLSI design flows, clocking methodologies, or physical design

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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