Senior STA Signoff Methodology Engineer

Posted One Month Ago
Be an Early Applicant
2 Locations
In-Office
168K-311K Annually
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Develops timing signoff strategies for advanced GPUs, CPUs, LPUs, and SoCs. Runs large-scale SPICE and STA experiments, builds STA and PNR flows, models advanced-node effects, and improves timing, power, yield, and reliability. Collaborates with technology, physical-design, and timing teams while developing automation and data-analysis methods using Python, JMP, Tcl, and industry-standard EDA tools. The role supports advanced FinFET and emerging CMOS technologies, including 3D IC integration and die stacking.
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.

We are seeking an innovative Senior STA Signoff Methodology Engineer to help drive sign-off strategies for the world's leading GPUs, CPUs, LPUs and SoCs. This position is a broad opportunity to optimize performance, yield, and reliability through increasingly comprehensive modeling, insightful analysis, and automation. This work will influence the entire next generation AI landscape through critical contributions across NVIDIA's many product lines. We have crafted a team of highly motivated people whose mission is to push the frontiers of what is possible today and define the platform for the future of computing. If you are fascinated by the immense scale of precision, craftsmanship, and artistry required to make billions of transistors function on every die at technology nodes as deep as 3nm and beyond, this is an ideal role.

What you'll be doing:

  • Run large-scale SPICE simulations and STA experiments to model the impact of advanced technologies on chip timing.

  • Develop STA and PNR flows and recommendations addressing aging, self-heating, thermal effects, IR drop, electro migration, and other advanced-node physical effects.

  • Collaborate with technology leads, physical-design engineers, and timing engineers to define and deploy sophisticated timing-signoff strategies for extraordinary silicon performance.

  • Develop tools and methodologies that improve design performance, predictability, and silicon reliability beyond the capabilities of standard EDA tools.

  • Work across STA, constraints, and timing and power optimization.

  • Perform extensive data analysis using Python, JMP, or similar tools to improve STA-to-silicon correlation.

What we need to see:

  • MS in Electrical or Computer Engineering, or equivalent experience, with 8 years of experience in ASIC design and timing.

  • Solid understanding of RC extraction, device physics, STA methodologies, and EDA-tool limitations.

  • Proven foundation in the mathematics and physics underlying electrical design.

  • Experience with low-power techniques, including multi-Vt design, clock gating, power gating, activity-based power analysis, DVFS, and CDC.

  • Understanding of signal and power integrity, crosstalk, electromigration, noise, OCV, timing margins, clock jitter, and IR drop.

  • Understanding of standard-cell, memory, and I/O IP modeling and their use in ASIC flows.

  • Hands-on experience with advanced FinFET and emerging CMOS technologies at 5 nm, 3 nm, 2 nm, and beyond.

  • Familiarity with industry-standard ASIC tools such as PrimeTime, ICC2, RedHawk, and Tempus.

  • Strong communication skills and a collaborative working style.

Ways to stand out from the crowd:

  • Familiarity with 3D IC integration, die stacking and packaging, self-heating, and their impact on timing closure.

  • Strong data-analysis and modeling skills employing Python, JMP, or similar platforms.

  • Proficiency in Tcl and Python; C++ experience is a plus.

NVIDIA is widely considered one of the technology world's most desirable employers. We have some of the most forward-thinking and talented people in the world working for us. If you're an engineer who thinks in systems, takes pride in designing things that last, and wants to see the silicon your work helps produce — we want to hear from you.

Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/ 

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 264,500 USD for Level 4, and 196,000 USD - 310,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until August 24, 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

  • Master’s degree in Electrical Engineering, Computer Engineering, or equivalent experience
  • 8 years of experience in ASIC design and timing
  • Strong understanding of RC extraction, device physics, STA methodologies, and EDA-tool limitations
  • Foundation in the mathematics and physics underlying electrical design
  • Experience with low-power techniques including multi-Vt design, clock gating, power gating, activity-based power analysis, DVFS, and CDC
  • Understanding of signal and power integrity, crosstalk, electromigration, noise, OCV, timing margins, clock jitter, and IR drop
  • Understanding of standard-cell, memory, and I/O IP modeling in ASIC flows
  • Hands-on experience with advanced FinFET and emerging CMOS technologies at 5 nm, 3 nm, 2 nm, and beyond
  • Familiarity with PrimeTime, ICC2, RedHawk, and Tempus
  • Strong communication skills and collaborative working style
  • Familiarity with 3D IC integration, die stacking, packaging, self-heating, and timing-closure impacts
  • Strong data-analysis and modeling skills using Python, JMP, or similar platforms
  • Proficiency in Tcl and Python
  • C++ experience

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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