Architecture Energy Modeling Engineer - New College Grad 2026

NVIDIA is seeking an Architecture Energy Modeling Engineer to join our Power Modeling, Methodology and Analysis Team. The successful candidate will be responsible for researching, developing, and deploying methodologies to help NVIDIA's products become more energy efficient. This includes 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.
• Collaborating 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.
• Working with architects, designers, and performance engineers to develop an energy-efficient GPU.
• Identifying key design features and workloads for building Machine Learning-based unit power/energy models.
• Developing and owning methodologies and workflows to train models using ML and/or statistical techniques.
• Improving the accuracy of trained models by using different model representations, objective functions, and learning algorithms.
• Developing methodologies to estimate data movement power/energy accurately.
• Correlating the predicted energy from models built at different stages of the design cycle, with the goal of bridging early estimates to silicon.
• Working with performance infrastructure teams to integrate power/energy models into their platforms to enable combined reporting of performance and power for various workloads.
• Developing tools to debug energy inefficiencies observed in various workloads run on silicon, RTL, and architectural simulators.
• Prototyping new architectural features, building an energy model for those new features, and analyzing the system impact.
• Identifying, suggesting, and/or participating in studies for improving GPU perf/watt.

The ideal candidate will have:

• A Master's or Ph.D. in Electrical Engineering, Computer Engineering, Computer Science, or equivalent experience.
• Strong coding skills, preferably in Python, C++.
• A background in machine learning, AI, and/or statistical modeling.
• A background in computer architecture and interest in energy-efficient GPU designs.
• Familiarity with Verilog and ASIC design principles is a plus.
• The ability to formulate and analyze algorithms, and comment on their runtime and memory complexities.
• A basic understanding of fundamental concepts of energy consumption, estimation, and low power design.
• A desire to bring quantitative decision-making and analytics to improve the energy efficiency of our products.
• Good verbal/written communication and interpersonal skills.

NVIDIA offers equity and benefits to its employees.