Software Development Engineer - GPU Kernel Development

About the Role

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

Requirements

• Skilled engineer with strong technical and analytical expertise in C++ development within Linux environments
• Experience in GPU Kernel Development & Optimization: designing and optimizing GPU kernels for deep learning on AMD GPUs using HIP, CUDA, and assembly (ASM)
• Knowledge of AMD architectures (GCN, RDNA) and low-level programming to maximize AI performance
• Experience with tools like Compute Kernel (CK), CUTLASS, and Triton for multi-GPU and multi-platform performance
• Experience in integrating GPU performance into machine learning frameworks such as TensorFlow and PyTorch
• Proficiency in Python and C++, including debugging, performance tuning, and test design
• Experience in high-performance computing, running large-scale workloads on heterogeneous compute clusters
• Foundational understanding of compiler theory and tools like LLVM and ROCm
• Academic background with a Bachelor's and/or Master's Degree in Computer Science, Computer Engineering, Electrical Engineering, or related fields

Nice to Have

• Experience 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
• Experience with tools like Compute Kernel (CK), CUTLASS, and Triton
• Deep Learning Integration experience with frameworks like TensorFlow and PyTorch
• Experience in large-scale workloads on heterogeneous compute clusters
• Knowledge of compiler optimization, LLVM, and ROCm

Qualifications

• Bachelor's and/or Master's Degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field

Benefits & Perks

• AMD benefits at a glance