Architecture Energy Modeling Engineer - New College Grad 2026

About the role

Our team is responsible for researching, developing, and deploying methodologies to help NVIDIA's products become more energy efficient. We build energy models that integrate into architectural simulators, RTL simulation, emulation, and silicon platforms. As an Architecture Energy Modeling Engineer, you will develop 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 provide early insight into energy consumption of graphics and artificial intelligence workloads, influencing architectural, design, and power management improvements.

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, bridging early estimates to silicon.
• Work with performance infrastructure teams to integrate power/energy models into their platforms for combined reporting of performance and power.
• Develop tools to debug energy inefficiencies observed in workloads run on silicon, RTL, and architectural simulators, and suggest solutions.
• Prototype new architectural features, build energy models for them, and analyze system impact.
• Identify, suggest, and/or participate in studies for improving GPU performance per watt.

Requirements

• Pursuing or recently completed an MS or PhD in Electrical Engineering, Computer Engineering, Computer Science, or equivalent experience.
• Strong coding skills, preferably in Python and 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 energy efficiency.
• Good verbal/written communication and interpersonal skills.

Pay

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.