Architecture Energy Modeling Engineer - New College Grad 2026

NVIDIA Corporation; Santa Clara (CA); Vor Ort; USD 136.000 - 219.000; Vollzeit

Benefits dieser Stelle

Equity compensation

Benefits

Zusammenfassung

NVIDIA Corporation in Santa Clara, California is seeking an Architecture Energy Modeling Engineer to join the Power Modeling, Methodology and Analysis Team. You will develop machine-learning powered energy models to study and reduce GPU power consumption and work with architects, designers, and performance engineers to improve perf/watt across NVIDIA’s GPUs and Tegra platforms.

The role involves prototyping energy models for new architectural features, training models with ML and statistics, and

Qualifikationen

• MS or PhD in Electrical/Computer Engineering, CS or equivalent experience.
• Strong coding skills in Python and C++.
• Background in ML, AI, or statistical modeling.
• Background in architecture and energy-efficient GPU design.
• Familiarity with Verilog and ASIC design is a plus.

Aufgaben

• Develop ML-based power models to analyze GPU energy consumption.
• Collaborate with Architects, ASIC, Low Power and Performance Engineers to study energy modeling.
• Develop and own methodologies to train models using ML or statistics.
• Improve model accuracy with different representations and learning algorithms.
• Integrate power/energy models into performance platforms for combined reporting.

Kenntnisse

Python

C++

Machine Learning

Computer Architecture

Verilog

Ausbildung

MS/PhD in Electrical Engineering / Computer Engineering / Computer Science

Tools

NVIDIA toolchains

Jobbeschreibung

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.

We are now looking for an Architecture Energy Modeling Engineer to join our Power Modeling, Methodology and Analysis Team!Our team is responsible for researching, developing, and deploying methodologies to help NVIDIA’s products become more energy efficient; and is responsible for building energy models that integrate into architectural simulators, RTL simulation, emulation and silicon platforms. Key responsibilities include developing Machine Learning based power models to analyze and reduce power consumption of NVIDIA GPUs. 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 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:

• Work with architects, designers, and performance engineers to develop an energy-efficient GPU.
• Identify key design features and workloads for building Machine Learning based unit power/energy models.
• Develop and own methodologies and workflows to train models using ML and/or statistical techniques.
• Improve the accuracy of trained models by using different model representations, objective functions, and learning algorithms.
• Develop methodologies to estimate data movement power/energy accurately.
• Correlate the predicted energy from models built at different stages of the design cycle, with the goal of bridging early estimates to silicon.
• Work with performance infrastructure teams to integrate power/energy models into their platforms to enable combined reporting of performance and power for various workloads.
• Develop tools to debug energy inefficiencies observed in various workloads run on silicon, RTL, and architectural simulators. Identify and suggest solutions to fix the energy inefficiencies.
• Prototype new architectural features, build an energy model for those new features, and analyze the system impact.
• Identify, suggest, and/or participate in studies for improving GPU perf/watt.

What we need to see:

• Pursuing or recently completed a MS or PhDin Electrical Engineering, Computer Engineering, Computer Science or equivalent experience.
• Strong coding skills, preferably in Python, C++.
• Background in machine learning, AI, and/or statistical modeling.
• Background in computer architecture and interest in energy-efficient GPU designs.
• Familiarity with Verilog and ASIC design principles is a plus.
• Ability to formulate and analyze algorithms, and comment on their runtime and memory complexities.
• Basic understanding of fundamental concepts of energy consumption, estimation, and low power design.
• Desire to bring quantitative decision-making and analytics to improve the energy efficiency of our products.
• Good verbal/written communication and interpersonal skills.

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!

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.

Applications for this job will be accepted at least until August 25, 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.