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 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 performance for AI operations.
• Experience with tools like Compute Kernel (CK), CUTLASS, and Triton for multi-GPU and multi-platform performance.
• Experience in integrating optimized GPU performance into machine learning frameworks such as TensorFlow and PyTorch.
• Proficiency in Python and C++, with experience in debugging, performance tuning, and test design.
• Solid experience in running large-scale workloads on heterogeneous compute clusters, optimizing for efficiency and scalability.
• Foundational understanding of compiler theory and tools like LLVM and ROCm.
• Bachelor's and/or Master's Degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field.

Nice to Have

• Experience in integrating GPU performance optimizations into machine learning frameworks to accelerate training and inference.
• Deep understanding of AMD architectures (GCN, RDNA) and low-level programming for performance maximization.
• Experience with performance tuning and debugging complex software solutions.
• Knowledge of compiler optimization techniques and tools like 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.