Our team is privileged to work on Power Optimization of Data center, gaming and automotive GPU chips. We also work on power optimization of Networking chips. The gamut of GPUs and networking chips requires the team to provide architecture, micro-architecture, RTL Design, methodology and AI based power optimization solutions. You will collaborate with Architects, Performance Engineers, Software Engineers, ASIC Design Engineers, and Physical Design teams to study and implement power analysis and reduction techniques for NVIDIA's next generation GPUs and Networking products. Your contributions will help us gain early insight into energy consumption of graphics and artificial intelligence workloads, and will allow us to influence architectural, design, and power management improvements.
What You'll Be Doing:
Use internally developed tools and industry standard pre-silicon gate-level and RTL power analysis tools, to help improve product power efficiency.
Use artificial intelligence to devliver RTL and/or Architecture Power optimization solutions.
Develop and share best practices for performing pre-silicon power analysis.
Perform comparative power analysis, to spot trends and anomalies, that warrant more scrutiny.
Interact with architects and RTL designers to help them interpret their power data and identify power bugs; drive them to implement fixes.
Select and run a wide variety of workloads for power analysis.
Prototype a new architectural feature in Verilog and analyze power.
What We Need To See:
Pursuing or recently completed a MS or PhD in Electrical Engineering, Computer Engineering, or related fields with coursework or experience in AI, Digital Design and VLSI concepts (or equivalent experience).
Understanding of RTL power optimization fundamentals, including switching activity, clock/enable efficiency, and common low‑power design patterns at the RTL level.
Knowledge of backend flows such as logic synthesis and place‑and‑route, and how RTL decisions impact post‑layout power and timing.
Familiarity with RTL implementation of low‑power techniques (e.g., clock gating, operand isolation, power gating strategies, multi‑VT usage) and their trade‑offs.
Exposure to industry power analysis tools such as PowerArtist, PrimeTime PX, or equivalent, including running power analysis and interpreting reports to guide design changes.
Strong Python programming skills for building automation scripts, data pipelines, and AI/ML‑driven analysis flows for RTL power optimization.
Coursework and/or hands‑on experience in Machine Learning and Artificial Intelligence, with ability to apply ML techniques to EDA and silicon power problems.
Previous experience debugging RTL or gate‑level power anomalies by tracing logic cones, examining activity/toggle data, and identifying root‑cause structures or scenarios.
Strong written and verbal communication skills to document methodologies, present power findings, and explain AI/ML‑based insights to both design and tools teams.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you!
116,000 USD – 189,750 USD for Level 2; and 136,000 USD – 218,500 USD for Level 3.
You will also be eligible for equity and benefits.
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
- Pursuing or recently completed MS or PhD in Electrical Engineering, Computer Engineering, or related field
- Understanding of RTL power optimization fundamentals (switching activity, clock/enable efficiency, low‑power RTL patterns)
- Knowledge of backend flows such as logic synthesis and place‑and‑route and their impact on power and timing
- Familiarity with RTL implementation of low‑power techniques (clock gating, operand isolation, power gating strategies, multi‑VT)
- Exposure to industry power analysis tools such as PowerArtist, PrimeTime PX, or equivalent
- Strong Python programming skills for automation, data pipelines, and AI/ML‑driven analysis
- Coursework and/or hands‑on experience in Machine Learning and Artificial Intelligence applied to EDA/silicon power problems
- Previous experience debugging RTL or gate‑level power anomalies and performing root‑cause analysis
- Strong written and verbal communication skills to document and present power findings
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.
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Equity Value & Accessibility — Company stock and RSUs have driven exceptional total compensation, with a generous employee stock purchase plan and ongoing equity refreshers playing a central role. Feedback suggests historic share-price gains created significant financial upside for many.
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Healthcare Strength — Multiple plan options (HSA, PPO, and in some locations Kaiser) with in-network preventive care covered at 100% and company HSA contributions indicate a robust offering. Mental-health sessions and broad dental, vision, and disability coverage further strengthen the package.
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Parental & Family Support — Up to 22 weeks of fully paid parental leave, adoption and surrogacy reimbursement, and coverage for IVF and egg freezing are highlighted as standout features. Additional family-care resources and caregiving support broaden the appeal.
NVIDIA Insights
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.”
