Principal Software Engineer — AI Performance & Reliability

Advanced Micro Devices, Inc•San Jose, CA

1d•Hybrid

About The Position

ADVANCE YOUR CAREER. ADVANCE THE WORLD. At AMD, we believe technology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future. Whether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger — technology that moves the world forward. Join us and, together, we’ll advance your career. THE ROLE: 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.

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.

Nice To Haves

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.

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.