英伟达System Software Engineer, AI Infrastructure - New College Grad 2026

• Build internal profiling/analysis tools for real world application perf/power analysis at system from small to large scale. • Build infrastructure or services for data visualization/mining and management. • Work with our users to build their perf/power models on top of our tools for next generation HW design.

At NVIDIA, we pride ourselves in having energy efficient products. We believe that continuing to maintain our products' energy efficiency compared to the competition is key to our continued success. 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, and emulation platforms. Key responsibilities include developing techniques to model, analyze, and reduce the power consumption of NVIDIA GPUs. As a member of the Power Modeling, Methodology, and Analysis Team, you will collaborate with Architects, Performance Engineers, Software Engineers, ASIC Design Engineers, and Physical Design teams to study and implement energy modeling techniques for NVIDIA's next-generation GPUs and Tegra SOCs. Your contributions will help us gain early insight into the 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 and performance architects to develop an energy-efficient GPU. • Develop methodologies and workflows to select and run a wide variety of workloads to train models using ML and/or statistical techniques. • Develop methodologies to improve the accuracy of energy models under various constraints, such as, process, timing, floorplan and layout. • Correlate the predicted energy from models created at different stages of the design cycle, with the goal of bridging early estimates to silicon. • Develop tools to debug energy inefficiencies observed in various workloads run on silicon, RTL and architectural simulators. Work with architects to fix the identified energy inefficiencies. • Work with performance, verification and emulation methodology and infrastructure development teams to integrate energy models into their platforms. • Prototype new architectural features, create an energy model, and analyze the system impact.

At NVIDIA, we pride ourselves in having energy efficient products. We believe that continuing to maintain our products' energy efficiency compared to the competition is key to our continued success. 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, and emulation platforms. Key responsibilities include developing techniques to model, analyze, and reduce the power consumption of NVIDIA GPUs. As a member of the Power Modeling, Methodology, and Analysis Team, you will collaborate with Architects, Performance Engineers, Software Engineers, ASIC Design Engineers, and Physical Design teams to study and implement energy modeling techniques for NVIDIA's next-generation GPUs and Tegra SOCs. Your contributions will help us gain early insight into the 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 and performance architects to develop an energy-efficient GPU. • Develop methodologies and workflows to select and run a wide variety of workloads to train models using ML and/or statistical techniques. • Develop methodologies to improve the accuracy of energy models under various constraints, such as, process, timing, floorplan and layout. • Correlate the predicted energy from models created at different stages of the design cycle, with the goal of bridging early estimates to silicon. • Develop tools to debug energy inefficiencies observed in various workloads run on silicon, RTL and architectural simulators. Work with architects to fix the identified energy inefficiencies. • Work with performance, verification and emulation methodology and infrastructure development teams to integrate energy models into their platforms. • Prototype new architectural features, create an energy model, and analyze the system impact.

Joining NVIDIA's DGX Cloud Team means contributing to the infrastructure that powers our innovative AI research. This team focuses on optimizing efficiency and resiliency of AI workloads, as well as developing scalable AI and Data infrastructure tools and services. Our objective is to deliver a stable, scalable environment for AI researchers, providing them with the necessary resources and scale to foster innovation. We are seeking an AI infrastructure software engineer to join our team. You'll be instrumental in designing, building, and maintaining AI infrastructure that enable large-scale AI training and inferencing. The responsibilities include implementing software and systems engineering practices to ensure high efficiency and availability of AI systems.As a senior DGX Cloud AI Infrastructure software engineer at NVIDIA, you will have the opportunity to work on innovative technologies that power the future of AI and data science, and be part of a dynamic and supportive team that values learning and growth. The role provides the autonomy to work on meaningful projects with the support and mentorship needed to succeed, and contributes to a culture of blameless postmortems, iterative improvement, and risk-taking. If you are seeking an exciting and rewarding career that makes a difference, we invite you to apply now! What you’ll be doing: • Develop infrastructure software and tools for large-scale AI, LLM, and GenAI infrastructure. • Develop and optimize tools to improve infrastructure efficiency and resiliency. • Root cause and analyze and triage failures from the application level to the hardware level • Enhance infrastructure and products underpinning NVIDIA's AI platforms. • Co-design and implement APIs for integration with NVIDIA's resiliency stacks. • Define meaningful and actionable reliability metrics to track and improve system and service reliability. • Skilled in problem-solving, root cause analysis, and optimization.