Architecture Energy Modeling Engineer - New College Grad 2026

NVIDIA•Austin, TX

About The Position

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.

Requirements

Pursuing or recently completed a MS or PhD in 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.
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.

Nice To Haves

Familiarity with Verilog and ASIC design principles is a plus.

Responsibilities

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.