英伟达Developer Technology Engineer, AI - New College Grad 2026

• Understand the responsibilities associated with embodied AI and strive to enhance them. • Develop on frameworks like IsaacSim and Isaac Lab, ensuring flawless performance. • Profile and investigate the performance of optimized code together with our internal team. • Discuss your approach and results with NVIDIA engineers to continuously improve processes. • Optimize GPU-based physics simulator performance for world-class results. • Collaborate closely with architecture, research, libraries, tools, and system software teams to invent and develop next-generation architectures, software platforms, and programming models.

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.

• Study and develop cutting-edge techniques in CUDA programming, profiling, optimization. Application domains include deep learning, graphic, machine learning, and data analytics, and perform in-depth analysis and optimization to ensure the best possible performance on current- and next-generation GPU architectures. • Work directly with key customers to understand the current and future problems they are solving and provide the best AI solutions using GPUs. • Collaborate closely with the architecture, research, libraries, tools, and system software teams at NVIDIA to influence the design of next-generation architectures, software platforms, and programming models.