Software R&D Engineer – GPU Kernel Development

Advanced Micro Devices, Inc•Orlando, FL

14h

About The Position

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.

Requirements

Proficient in C++ programming, C++17 and/or C++20.
Experienced in designing and optimizing GPU kernels on AMD/Nvidia GPUs using HIP, CUDA, and/or assembly (ASM).
Experienced in low-level programming to maximize performance for AI operations, leveraging tools like Compute Kernel (CK) and/or CUTLASS for multi-GPU and multi-platform performance.
Experience using profiling and benchmark tooling for large models.
Excellent problem-solving skills and attention to detail.
Strong communication and teamwork skills.

Nice To Haves

Experience in C++23/26.
Experience with model optimization, such as low-precision quantization (MXFP4, FP8, INT4).
Experience with AI software frameworks, such as PyTorch, vLLM, SGLang, etc.
Knowledge of model architectures, LLMs, MoE, and diffusion.
Knowledge of AMD architectures (GCN, RDNA, CDNA)
Experience in running large-scale HPC workloads on heterogeneous compute clusters, optimizing for efficiency and scalability.
Knowledge of compiler theory and tools like LLVM and ROCm for kernel and system performance optimization.

Responsibilities

Develop GPU Kernels: Create and optimize GPU kernels to maximize performance for specific ML/AI/HPC workloads on pre-silicon architectures.
Develop & Optimize Models: Design and optimize low-level GPU kernels to accelerate inference and training of large machine learning models. Maximize computational efficiency and reduce execution time while ensuring model accuracy.
Multi-GPU and Multi-Node Optimization: Design and implement strategies for distributed model training and inference across multiple GPUs and nodes. Address data parallelism and model parallelism challenges to fully utilize available resources.
Performance Profiling: Profile and analyze system and application performance to identify bottlenecks and areas for improvement. Use profiling tools to understand and optimize hardware resource utilization.
Parallel Computing: Leverage parallel computing techniques to improve the scalability and performance of machine learning workloads. Implement multi-threading and GPU synchronization techniques.
Benchmarking and Testing: Develop benchmarks and testing procedures to assess the performance and stability of optimized models and frameworks. Ensure that the solutions meet or exceed the defined performance criteria.
ML Frameworks: Use, enhance and optimize frameworks like PyTorch, vLLM, SGLang for AMD GPUs in open-source repositories.
Collaborate with GPU Library Teams: Work closely with internal teams to analyze and improve workloads performance on AMD GPUs and pre-silicon architectures.
Software Engineering Best Practices: Apply sound engineering principles to ensure robust, maintainable solutions.
Documentation: Create detailed documentation of optimizations, best practices, and implementation guidelines to facilitate knowledge sharing and maintainable code.