As a member of the Architecture Energy Modeling Team, you will collaborate with Architects, ASIC Design Engineers, Low Power Engineers, Performance Engineers, Software Engineers, and Physical Design teams to study and implement energy modeling techniques for NVIDIA's next generation GPUs, CPUs and Tegra SOCs. Your contributions will help us understand energy usage in graphics and AI workloads and make improvements in architecture, design, and power management.
Today, NVIDIA is 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, encouraging environment where everyone is inspired to do their best work. Come join the team and see how we can make a lasting impact on the world!
What you'll be doing:
Define and implement tools and methodologies for efficient data generation from post layout netlists to feed into data movement power analytical model.
Develop tools and infrastructure to sanitize each metric in the model to achieve high correlation accuracy.
Define and implement tools and methodologies for efficient integration of power models with performance tools.
Identify runtime and memory limitation of existing flows and tools to speedup model delivery process.
Mine data from pre- and post-silicon performance runs to find important data paths and bottlenecks. Give feedback to design teams and improve power efficiency.
Work with floorplan, performance, verification and emulation methodology and infrastructure development teams to integrate data movement power models.
Experiment with various ML techniques to answer what-if design questions and set proper power/energy targets for next generation chips.
Enable efficient storage and retrieval of data from database.
Enable easy visualization of data using platforms such as PowerBI, OpenSearch.
What we need to see:
Pursuing or recently completed a MS or PhD in Electrical or Computer Engineering or equivalent experience.
Strong coding skills, preferably in Python, C++.
Ability to formulate and analyze algorithms, and comment on their runtime and memory complexities.
Understanding of VLSI, digital design, and computer architecture concepts.
Basic understanding of fundamental concepts of power and energy consumption, estimation, and low power design.
Basic understanding of chip design process from RTL design to tape-out.
Background in machine learning, AI, and/or statistical modeling is a plus.
Desire to bring quantitative decision-making and analytics to improve the energy efficiency of our products.
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. Are you creative and autonomous? Do you love a challenge? 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 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 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.Skills Required
- Pursuing or recently completed a MS or PhD in Electrical or Computer Engineering or equivalent experience
- Strong coding skills, preferably in Python, C++
- Ability to formulate and analyze algorithms, and comment on their runtime and memory complexities
- Understanding of VLSI, digital design, and computer architecture concepts
- Basic understanding of fundamental concepts of power and energy consumption
- Basic understanding of chip design process from RTL design to tape-out
- Background in machine learning, AI, and/or statistical modeling
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 — 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.
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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.
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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.
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.”
