AMDAI Software Development Engineer
任职要求
Expert knowledge and hands-on experience in C, C++, and Python. Solid understanding of object-oriented design principles. Familiarity with LLM and Stable Diffusion model architectures. Strong analytical and problem-solving skills. Academic Credentials: Bachelor’s or Master’s degree in Computer Science, Computer/Software Engineering, or a related technical discipline. #LI-FL1
工作职责
Key Responsibilities: Develop and integrate hardware kernels and contribute to NPU runtime development. Collaborate with the hardware team to identify and resolve functional and performance issues. Lead the end-to-end deployment and optimization of LLM and Stable Diffusion models. Work closely with customers to support the development of new features and performance improvements, ensuring timely delivery.
THE ROLE: Triton is a language and compiler for writing highly efficient custom deep learning primitives. It's widely adopted in open AI software stack projects like PyTorch, vLLM, SGLang, and many others. AMD GPU is an official backend in Triton and we are fully committed to it. If you are interested in making GPUs running fast via developing the Triton compiler and kernels, please come join us!
THE ROLE: AMD is looking for a world class AI frameworks engineer who can provide technical leadership in the development of various AI frameworks in the AMD ecosystem. You will need to drive technical direction for next generation frameworks for AI model training and inference for wide variety of AMD devices, current and future, such as MI Instinct, and Radeon GPUs, XDNA devices, including the recently released Ryzen AI, Alveo V70 and Versal ACAP, and datacenter CPUs such as EPYC. You will work enhance the AI framework capabilities to enable cutting-edge models on onto AMD’s cutting-edge hardware.
THE ROLE: MTS Software development engineer on teams building and optimizing Deep Learning applications and AI frameworks for AMD GPU compute platforms. Work as part of an AMD development team and open-source community to analyze, develop, test and deploy improvements to make AMD the best platform for machine learning applications. THE PERSON: Strong technical and analytical skills in C++ development in a Linux environment. Ability to work as part of a team, while also being able to work independently, define goals and scope and lead your own development effort. KEY RESPONSIBILITIES: Optimize Deep Learning Frameworks: In depth experience in enhance and optimize frameworks like TensorFlow and PyTorch for AMD GPUs in open-source repositories. Develop GPU Kernels: Create and optimize GPU kernels to maximize performance for specific AI operations. Develop & Optimize Models: Design and optimize deep learning models specifically for AMD GPU performance. Collaborate with GPU Library Teams: Work tightly with internal teams to analyze and improve training and inference performance on AMD GPUs. Collaborate with Open-Source Maintainers: Engage with framework maintainers to ensure code changes are aligned with requirements and integrated upstream. Work in Distributed Computing Environments: Optimize deep learning performance on both scale-up (multi-GPU) and scale-out (multi-node) systems. Utilize Cutting-Edge Compiler Tech: Leverage advanced compiler technologies to improve deep learning performance. Optimize Deep Learning Pipeline: Enhance the full pipeline, including integrating graph compilers. Software Engineering Best Practices: Apply sound engineering principles to ensure robust, maintainable solutions.
• 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 TensorRT-LLM • Provide feedback into the architecture and hardware design and development • Collaborate across the company to guide the direction of machine learning inferencing, working with software, research and product teams • Publish key results in scientific conferences