SR. Software Development Engineer - GPU Kernel Development

What You Do At AMD Changes Everything At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you'll discover the real differentiator is our culture. We push the limits of innovation to solve the world's most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career. The Role As a core member of the team, you will play a pivotal role in optimizing and developing deep learning frameworks for AMD GPUs. Your expertise will be critical in enhancing GPU kernels, deep learning models, and training/inference performance across multi-GPU and multi-node systems. You will engage with both internal GPU library teams and open-source maintainers to ensure seamless integration of optimizations, utilizing cutting-edge compiler technologies and advanced engineering principles to drive continuous improvement. The Person Seeking an industry leading expert C++ developer with advanced technical and analytical skills in Linux environments. The ideal candidate will excel in providing technical leadership, guiding teams, and driving projects/initiatives independently. You will define goals, scope, and own development efforts while collaborating effectively within a high-performing team. Key Responsibilities Optimize Deep Learning Frameworks: 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 closely 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. Lead, Guide & Mentor: Provide strategic direction and mentorship to junior team members, fostering growth and collaboration through code reviews, knowledge sharing, and technical guidance. Preferred Experience GPU Kernel Development & Optimization: Deep expertise in designing and optimizing GPU kernels for deep learning on AMD GPUs using HIP, CUDA, and assembly (ASM). Strong knowledge of AMD architectures (GCN, RDNA) and low-level programming to maximize performance for AI operations, leveraging tools like Compute Kernel (CK), CUTLASS, and Triton for multi-GPU and multi-platform performance. Deep Learning Integration: Proven ability and experience to integrate GPU-accelerated compute into ML frameworks (e.g., PyTorch, TensorFlow), with a focus on throughput, scalability, and efficient execution for training and inference workloads. Software Engineering Excellence: Advanced proficiency in Python and C++ with deep experience in performance tuning, debugging, and robust test design, ensuring reliable, maintainable, high-performance codebases. High-Performance Computing: Broad and in-depth experience with large-scale, heterogeneous compute environments; adept at optimizing AI workloads for performance, efficiency, and resource utilization across clusters. Compiler Optimization: Thorough and detailed understanding of compiler internals, LLVM, and ROCm, with the ability to drive system-level optimizations from source to machine code. Academic Credentials Master's and/ PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field.