英伟达Deep Learning Performance Architect - Intern - 2026
任职要求
• BS or higher degree in a relevant technical field (CS, EE, CE, Math, etc.). • Strong programming skills in Python, C, C++. • Strong background in computer architecture. • Experience with performance modeling, architecture simulation, profiling, and analysis. • Prior experience with LLM or generative AI algorithms. Ways to stand out from the crowd: • GPU Computing and parallel programming models such as CUDA and OpenCL. • Architecture of or workload analysis on other deep learning accelerators. • Deep neural network training, inference and optimization in …
工作职责
NVIDIA is developing processor and system architectures that accelerate deep learning and high-performance computing applications. We are looking for an intern deep learning system performance architect to join our AI performance modelling, analysis and optimization efforts. In this position, you will have a chance to work on DL performance modelling, analysis, and optimization on state-of-the-art hardware architectures for various LLM workloads. You will make your contributions to our dynamic technology focused company. What you’ll be doing: • Analyze state of the art DL networks (LLM etc.), identify and prototype performance opportunities to influence SW and Architecture team for NVIDIA's current and next gen inference products. • Develop analytical models for the state of the art deep learning networks and algorithm to innovate processor and system architectures design for performance and efficiency. • Specify hardware/software configurations and metrics to analyze performance, power, and accuracy in existing and future uni-processor and multiprocessor configurations. • Collaborate across the company to guide the direction of next-gen deep learning HW/SW by working with architecture, software, and product teams.
• Design, develop, and optimize major layers in LLM (e.g attention, GEMM, inter-GPU communication) for NVIDIA's new architectures. • Implement and fine-tune kernels to achieve optimal performance on NVIDIA GPUs. • Conduct in-depth performance analysis of GPU kernels, including Attention and other critical operations. • Identify bottlenecks, optimize resource utilization, and improve throughput, and power efficiency • Create and maintain workloads and micro-benchmark suites to evaluate kernel performance across various hardware and software configurations. • Generate performance projections, comparisons, and detailed analysis reports for internal and external stakeholders. • Collaborate with architecture, software, and product teams to guide the development of next-generation deep learning hardware and software.
We are now looking for a Deep Learning Performance Software Engineer! We are expanding our research and development for Inference. We seek excellent Software Engineers and Senior Software Engineers to join our team.We specialize in developing GPU-accelerated Deep learning software. Researchers around the world are using NVIDIA GPUs to power a revolution in deep learning, enabling breakthroughs in numerous areas. Join the team that builds software to enable new solutions. Collaborate with the deep learning community to implement the latest algorithms for public release in Tensor-RT. Your ability to work in a fast-paced customer-oriented team is required and excellent communication skills are necessary. What you’ll be doing: • Develop highly optimized deep learning kernels for inference • Do performance optimization, analysis, and tuning • Work with cross-collaborative teams across automotive, image understanding, and speech understanding to develop innovative solutions • Occasionally travel to conferences and customers for technical consultation and training
NVIDIA is developing processors and system architectures that accelerate deep learning on edge devices, workstations and data center GPUs for a variety of applications, including automotive, robotics, large language models (LLMs) and AI generative models. We are looking for an expert deep learning system performance architect to join our modelling, efficiency optimization, performance projections and analysis effort. In this position, you will have the chance to optimize deep learning hardware and software architecture and make the significant impact in a dynamic technology focused company What you’ll be doing :• Analyze performance and efficiency of various machine learning/deep learning algorithms on different architectures • Identify architecture and software performance bottlenecks and propose optimizations • Explore new features and hardware capabilities on deep learning applications
• Analyze state of the art DL networks (LLM etc.), identify and prototype performance opportunities to influence SW and Architecture team for NVIDIA's current and next gen inference products • Develop analytical models for the state of the art deep learning networks and algorithm to innovate processor and system architectures design for performance and efficiency. • Specify hardware/software configurations and metrics to analyze performance, power, and accuracy in existing and future uni-processor and multiprocessor configurations. • Collaborate across the company to guide the direction of next-gen deep learning HW/SW by working with architecture, software, and product teams.