Principal GPU Performance Engineer - Artificial Intelligence

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We care deeply about transforming lives with AMD technology to enrich our industry, our communities, and the world. Our mission is to build great products that accelerate next-generation computing experiences – the building blocks for the data center, artificial intelligence, PCs, gaming and embedded. Underpinning our mission is the AMD culture. We push the limits of innovation to solve the world’s most important challenges. We strive for execution excellence while being direct, humble, collaborative, and inclusive of diverse perspectives. 

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THE ROLE: 

We are seeking a GPU Performance Engineer to optimize AI training workloads and guide the evolution of next-generation AMD Instinct GPU architectures. In this role, you will work across the software and hardware stack to profile, analyze, and improve the efficiency of training foundation models on large-scale GPU clusters. You will collaborate with framework developers, distributed systems engineers, and hardware architects to ensure our GPUs deliver industry-leading training performance today while shaping the designs of tomorrow.

THE PERSON: 

We value curiosity and innovation, and we're committed to providing a challenging and supportive environment where you can learn and grow. As you collaborate with your peers, you'll have the opportunity to make a real impact and contribute to our organization's success.

KEY RESPONSIBILITIES: 

• Profile and optimize large-scale AI training workloads (transformers, multimodal, diffusion, recommender systems) across multi-node, multi-GPU clusters.
• Identify bottlenecks in compute, memory, interconnects, and communication libraries (NCCL/RCCL, MPI), and deliver optimizations to maximize scaling efficiency.
• Collaborate with compiler/runtime teams to improve kernel performance, scheduling, and memory utilization.
• Develop and maintain benchmarks and traces representative of foundation model training workloads.
• Provide performance insights to AMD Instinct GPU architecture teams, informing hardware/software co-design decisions for future architectures.
• Partner with framework teams (PyTorch, JAX, TensorFlow) to upstream performance improvements and enable better scaling APIs.
• Present findings to cross-functional teams and leadership, shaping both software and hardware roadmaps.

PREFERRED EXPERIENCE: 

• Strong expertise in GPU tuning and optimization (CUDA, ROCm, or equivalent).
• Understanding of GPU microarchitecture (execution units, memory hierarchy, interconnects, warp scheduling).
• Hands-on experience with distributed training frameworks and communication libraries (e.g., PyTorch DDP, DeepSpeed, Megatron-LM, NCCL/RCCL, MPI).
• Advanced Linux OS, container (e.g. Docker) and GitHub skills
• Proficiency in Python or C for performance-critical development.
• Familiarity with large-scale AI training infrastructure (NVLink, InfiniBand, PCIe, cloud/HPC clusters).
• Experience in benchmarking methodologies, performance analysis/profiling (e.g. Nsight), performance monitoring tools.
• Experience scaling training to thousands of GPUs for foundation models a plus.
• Strong track record of optimizing large-scale AI systems in cloud or HPC environments is desired.

ACADEMIC CREDENTIALS: 

• Master's or PhD degree in Computer Science or Computer Engineering

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Benefits offered are described:  AMD benefits at a glance.

AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law.   We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.