Senior Member of Technical Staff Software Engineer- GPU, LLM, AI

THE OPPORTUNITY

As a Senior Member of Technical Staff (SMTS), you will be at the heart of AMD's AI strategy, tackling one of the most exciting challenges in the industry: training and running AI to make AI itself more efficient on GPUs on the fly, which can dramatically alter the trajectory of AI progress. This is a high-impact, hands-on role where your work will directly define the software that powers the future of AI.

In this position, you will:

• Architect and Drive the AI Software Stack: You will establish best practices and optimize performance from the lowest-level GPU kernels to large-scale distributed systems, shaping the foundational software for AMD hardware. By leveraging cutting-edge Large Language Models (LLMs) and agent-based technologies, you will accelerate the development and performance enhancement of the AMD ROCm ecosystem, ensuring it remains at the forefront of AI innovation.

• Accelerate Foundational Models: Your work will directly accelerate cutting-edge applications like foundation models (LLMs) and autonomous AI agents, ensuring AMD is the platform of choice for the most demanding workloads.

• Innovate Across Hardware and Software: You will contribute to the entire co-design lifecycle, from influencing future GPU architectures to developing groundbreaking software for new accelerators and collaborating with the broader AI community.

Success in this role requires a deep passion for software engineering, strong technical ownership to see complex problems through to resolution, and the ability to influence technical direction across teams. As a senior engineer, you will also be expected to mentor others and effectively communicate your ideas to shape the future of AI at AMD.

CORE COMPETENCIES

To excel in this role, we seek a candidate with exceptional technical expertise, who can bridge deep proficiency in high-performance C++ software engineering and low-level GPU programming with a robust understanding of Large Language Models (LLMs) and AI systems. The ideal candidate can bridge kernel engineering with AI post-training (RL) experience. A great candidate is deep in one and light on the other.

Kernel engineering means demonstrating mastery in designing complex, scalable systems using modern C++, coupled with a fundamental grasp of GPU architectures (HIP/CUDA), memory hierarchies, and kernel optimization to maximize hardware performance. This expertise should be evidenced by significant hands-on experience in large-scale C++/HIP/CUDA projects, such as contributing to the ROCm ecosystem (e.g., rocBLAS, hipDNN, Composable Kernel, AITemplate), CUDA libraries (e.g., cuBLAS, cuDNN, CUTLASS, Thrust, CUB, NCCL), or the C++/HIP/CUDA core of ML frameworks like PyTorch, TensorFlow, or JAX.

AI post-training is equally critical, and requires deep understanding of LLMs, including but not limited to transformer architectures, attention mechanisms, and the full model lifecycle, with hands-on experience in advanced model alignment and post-training techniques like Supervised Fine-Tuning (SFT) and Reinforcement Learning (e.g., RLHF, GRPO). Candidates must also stay at the forefront of LLM advancements, showing familiarity with cutting-edge trends such as Mixture-of-Experts (MoE) architectures, inference optimizations (e.g., quantization, speculative decoding), and modern application patterns like Agentic AI systems (e.g. AlphaEvolve for code/kernel generation).

Experience and interest in code generation and/or self-improving LLMs is a plus.

QUALIFICATIONS FOR SUCCESS

This is a senior role that requires a unique blend of expertise across software engineering, GPU computing, and artificial intelligence. The ideal candidate will possess:

• Extensive professional software development experience in performance-critical environments.

• Long term hands-on experience in GPU programming (HIP/CUDA) and optimizing deep learning kernels and operators.

• A fundamental understanding of GPU architecture and memory hierarchy, used to diagnose and resolve complex performance bottlenecks.

• Expert-level proficiency in modern C++ and object-oriented design.

• Deep experience using GPU profiling and performance analysis tools (e.g., AMD ROCm Profiler, NVIDIA Nsight) to diagnose and resolve complex bottlenecks in distributed, multi-GPU systems.

• Deep knowledge of transformer architectures, attention mechanisms, and modern AI systems (Generative AI, Agentic AI).

• Hands-on experience optimizing the post-training and inference pipelines of Large Language Models (LLMs).

• Strong technical ownership, communication, and problem-solving skills with a track record of delivering complex technical solutions.

• Plus: Experience or deep expertise with the AMD ROCm/HIP ecosystem.

ACADEMIC CREDENTIALS

• Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent.

• Master's degree preferred, PhD is a plus.

• Relevant publications in AI/ML, GPU computing, or system optimization are highly valued.

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