Fellow Software Engineer — AI Performance & Reliability

ADVANCE YOUR CAREER. ADVANCE THE WORLD.

At AMD, we believe technology can change lives for the better. It can heal us, entertain us, and make us more connected, productive, and understanding of the world around us. And we’re looking for talent who feel the same: people who want to leave the planet better than they found it, those who don’t shy away from humanity’s challenges but are determined to help solve them.

AMD is powering the next generation of supercomputing, high-performance computing, cloud, and AI. Whether you’re designing next-gen processors, enabling AI breakthroughs, or creating go-to-market plans, every role at AMD contributes to something bigger — technology that moves the world forward.

We are looking for a strong, Principal or Fellow level software engineer to join our AI Infrastructure team. You will work on improving the performance, efficiency, and reliability of AI workloads across both model training and inference.

Our team supports a broad range of machine learning systems, including large language models, diffusion models, and recommendation models. You will collaborate closely with customers and internal engineering teams to understand performance bottlenecks, optimize workloads, and ensure that models run reliably at scale.

This role is a strong fit for an engineer who enjoys working across the AI software and hardware stack, solving technically challenging performance problems, and partnering directly with customers to make them successful.

You will help customers achieve meaningful improvements in model performance and system reliability. You will identify difficult bottlenecks, develop reusable solutions, and help shape the infrastructure and product capabilities needed to run demanding AI workloads efficiently at scale.

• Profile and optimize AI model training and inference workloads.
• Improve model throughput, latency, memory efficiency, scalability, and reliability.
• Identify bottlenecks across models, frameworks, compilers, runtimes, operating systems, and hardware.
• Optimize workloads involving large language models, diffusion models, recommendation systems, and other modern machine learning architectures.
• Develop performance tooling, benchmarks, automation, and observability systems.
• Investigate and resolve complex production issues affecting AI workloads.
• Collaborate with customers to understand their technical requirements, reproduce issues, and recommend effective solutions.
• Translate customer feedback into product and infrastructure improvements.
• Work closely with machine learning engineers, systems engineers, hardware teams, and product teams.
• Document performance findings, technical recommendations, and best practices.

• Strong software engineering skills and experience building production-quality systems.
• Experience working with AI infrastructure for model training, inference, or both.
• Demonstrated experience profiling and optimizing machine learning models or AI workloads.
• Strong foundations in computer architecture, including processors, memory hierarchies, parallelism, and performance tradeoffs.
• Solid understanding of systems performance concepts such as latency, throughput, memory bandwidth, utilization, and distributed communication.
• Proficiency in languages such as Python, C++, or similar systems-oriented programming languages.
• Experience with machine learning frameworks such as PyTorch, Tensor Flow, or JAX.
• Strong debugging and analytical skills, with the ability to investigate problems across multiple layers of the technology stack.
• Clear written and verbal communication skills.
• A customer-focused mindset and willingness to work directly with customers through technical evaluations, deployments, troubleshooting, and ongoing support.

• Experience optimizing large language models, diffusion models, or recommendation models.
• Experience with GPU, accelerator, or distributed computing environments.
• Familiarity with technologies such as ROCm, HIP, CUDA, Triton, XLA, MLIR, NCCL, or similar performance-oriented tools and runtimes.
• Experience with distributed training, model serving, quantization, compilation, kernel…