Performance Modeling Architect- Data Center GPU

Advanced Micro Devices, Inc • Santa Clara, CA

97d

About The Position

At AMD, our mission is to build great products that accelerate next-generation computing experiences – from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career.

Requirements

• Deep experience optimizing large-scale ML systems and GPU architectures
• Strong track record of technical leadership in GPU performance and workload analysis including patents and recent publications, participation in industry forums and peer acknowledgement
• Deep expertise in CUDA programming, GPU memory hierarchies, and hardware-specific optimizations
• Proven track record architecting distributed training systems handling large scale systems
• Expert knowledge of transformer architectures, attention mechanisms, and model parallelism techniques

Nice To Haves

• PyTorch, CUDA, TensorRT, OpenAI Triton
• Distributed systems: Ray, Megatron-LM
• Performance analysis tools: NSight Compute, nvprof, PyTorch Profiler
• KV cache optimization, Flash Attention, Mixture of Experts
• High-speed networking: InfiniBand, RDMA, NVLink

Responsibilities

• Lead performance modeling and optimization for multi-trillion parameter LLM training/inference including Dense, Mixture of Experts (MoE) with multiple modalities (text, vision, speech)
• Model/optimize novel parallelization strategies across tensor, pipeline, context, expert and data parallel dimensions
• Architect memory-efficient training systems utilizing techniques like structured pruning, quantization (MX formats), continuous batching/chunked prefill, speculative decoding
• Incorporate and extend SOTA models such as GPT-4, Reasoning models (Deepseek-R1), and multi-modal architectures
• Collaborate with internal and external stakeholders/ML researchers to disseminate results and iterate at rapid pace

Benefits

• AMD benefits at a glance

What This Job Offers

• Career Level: Senior
• Education Level: Master's degree
• Number of Employees: 5,001-10,000 employees