THE ROLE:AMD is looking for an influential software engineer who is passionate about improving the performance of key applications and benchmarks. You will be a member of a core team of incredibly talented industry specialists and will work with the very latest hardware and software technology.
THE PERSON:As a Staff Software Developer, 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.
KEY RESPONSIBILITIES: 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.
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., rpp, MIVisionX, rocAL, rocdecode, rocjpeg), CUDA libraries (e.g., CV-CUDA, cuDNN, 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.
PREFERRED EXPERIENCE: - 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:
- Lengthy professional software development experience in performance-critical environments.
Extensive hands-on experience in GPU programming (HIP/CUDA) and optimizing deep learning kernels and operators. - Computer vision expertise
- 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 or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent
- Relevant publications in AI/ML, GPU computing, or system optimization are highly valued.
This role is not eligible for visa sponsorship.Benefits offered are described: AMD benefits at a glance.
