Fellow Software Engineer — AI Performance & Reliability

AMD is powering the next generation of supercomputing and AI. They are seeking a Fellow Software Engineer to enhance the performance and reliability of AI workloads, collaborating with various teams to optimize and troubleshoot systems.

Responsibilities:

• 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

Requirements:

• 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, TensorFlow, 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
• A PhD (or a master's degree with equivalent experience) in artificial intelligence, machine learning, computer science, or a related field
• 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 optimization, or memory optimization
• Experience operating AI systems in production environments
• Prior experience in solutions engineering, field engineering, developer relations, or another customer-facing technical role
• Experience designing benchmarks and conducting systematic performance analysis