SAPSAP China iXp Intern - Generative AI Developer - Shanghai

We are looking for a Generative AI Intern Engineer to join the NVIDIA Developer Technology group (Devtech) and work with a team of experienced engineers on innovative uses of AI for games and content creation. The Devtech team works with NVIDIA researchers and leading game developers to bring cutting edge AI research from across NVIDIA and the industry to gamers and 3D professionals in high performance packages such as real-time inferenced graphics, physics and animations. What you’ll be doing: • Research and implement innovative generative AI algorithms for game engines and authoring tools, including real-time neural graphics, physics based animation and diffusion models. • Develop neural graphics, animation and physics models and maintain open-source projects for both game-making and user runtimes. Integrate them into mainstream game engines and DCC tools. • Use various optimization techniques, such as tensor fusion and quantization, to fit the AI models onto user devices and maximize the performance of inference for real-time gaming. • Collaborate with game developers on optimizations and improvements for specific GenAI applications. • Interact closely with the architecture and driver teams at NVIDIA in ensuring the best possible experience on current generation hardware, and on determining trends and features for next generation architectures.

THE ROLE: We are seeking a talented Machine Learning Kernel Developer to design, develop, and optimize low-level machine learning kernels for AMD GPUs using the ROCm software stack. In this role, you will work on high-impact projects to accelerate AI frameworks and libraries, with a focus on emerging technologies like Large Language Models (LLMs) and other generative AI workloads. THE PERSON: The ideal candidate will have hands-on experience with GPU programming (ROCm or CUDA) and a passion for pushing the boundaries of AI performance. KEY RESPONSIBILITIES: Design and implement highly optimized ML kernels (e.g., matrix operations, attention mechanisms) for AMD GPUs using ROCm. Profile, debug, and tune kernel performance to maximize hardware utilization for AI workloads. Collaborate with ML researchers and framework developers to integrate kernels into AI frameworks (e.g., PyTorch, TensorFlow) and inference engines (e.g., vLLM, SGLang). Contribute to the ROCm software stack by identifying and resolving bottlenecks in libraries like MIOpen, BLAS, or Composable Kernel. Stay updated on the latest AI/ML trends (LLMs, quantization, distributed inference) and apply them to kernel development. Document and communicate technical designs, benchmarks, and best practices. Troubleshoot and resolve issues related to GPU compatibility, performance, and scalability. REQUIRED EXPERIENCE: 2+ years of experience in GPU kernel development for machine learning (ROCm or CUDA). Proficiency in C/C++ and Python, with experience in performance-critical programming. Strong understanding of ML frameworks (PyTorch, TensorFlow) and GPU-accelerated libraries. Basic knowledge of modern AI technologies (LLMs, transformers, inference optimization). Familiarity with parallel computing, memory optimization, and hardware architectures. Problem-solving skills and ability to work in a fast-paced environment.

THE ROLE: We are seeking a talented Machine Learning Kernel Developer to design, develop, and optimize low-level machine learning kernels for AMD GPUs using the ROCm software stack. In this role, you will work on high-impact projects to accelerate AI frameworks and libraries, with a focus on emerging technologies like Large Language Models (LLMs) and other generative AI workloads.  THE PERSON: The ideal candidate will have hands-on experience with GPU programming (ROCm or CUDA) and a passion for pushing the boundaries of AI performance. KEY RESPONSIBILITIES: Design and implement highly optimized ML kernels (e.g., matrix operations, attention mechanisms) for AMD GPUs using ROCm. Profile, debug, and tune kernel performance to maximize hardware utilization for AI workloads. Collaborate with ML researchers and framework developers to integrate kernels into AI frameworks (e.g., PyTorch, TensorFlow) and inference engines (e.g., vLLM). Contribute to the ROCm software stack by identifying and resolving bottlenecks in libraries like MIOpen, HIP, or Composable Kernel. Stay updated on the latest AI/ML trends (LLMs, quantization, distributed inference) and apply them to kernel development. Document and communicate technical designs, benchmarks, and best practices. Troubleshoot and resolve issues related to GPU compatibility, performance, and scalability. REQUIRED EXPERIENCE: 2+ years of experience in GPU kernel development for machine learning (ROCm or CUDA). Proficiency in C/C++ and Python, with experience in performance-critical programming. Strong understanding of ML frameworks (PyTorch, TensorFlow) and GPU-accelerated libraries. Basic knowledge of modern AI technologies (LLMs, transformers, inference optimization). Familiarity with parallel computing, memory optimization, and hardware architectures. Problem-solving skills and ability to work in a fast-paced environment.

NVIDIA has continuously reinvented itself over two decades. NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world.This is our life’s work — to amplify human imagination and intelligence. AI becomes more and more important in Auto Driving and AI City. NVIDIA is at the forefront of the Auto Driving and AI City revolution and providing powerful solutions for them. All these solutions are based on GPU-accelerated libraries, such as CUDA, TensorRT and V/LLM inference framework etc. Now, we are now looking for an LLM inference framework developer engineer based in Shanghai. What you’ll be doing :• Craft and develop robust inferencing software that can be scaled to multiple platforms for functionality and performance • Performance analysis, optimization and tuning • Closely follow academic developments in the field of artificial intelligence and feature update • Collaborate across the company to guide the direction of machine learning inferencing, working with software, research and product teams