Principal AI Modeling Architect

AMD Principal Engineer Role

What You Do At AMD Changes Everything

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.

As a Principal Engineer, you will spearhead the next generation of AI infrastructure by defining GPU architecture specifications that enable massive model training r expertise will drive 2-3x performance gains in both training and inference pipelines through innovative system design and optimization. You will champion the adoption of cutting-edge techniques across the engineering organization, from efficient attention mechanisms to advanced parallelization strategies.

By establishing comprehensive best practices for distributed ML systems, you will create a framework that enables seamless scaling from single-GPU to thousand-GPU deployments.

You have a deep understanding of GPU microarchitecture, memory hierarchies, and their impact on large-scale ML workloads. You are passionate about software engineering and possess leadership skills to drive sophisticated issues to resolution. You are able to communicate effectively and work optimally with different teams across AMD.

• 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.

• Extensive and senior experience optimizing large-scale ML systems and GPU architectures
• 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

• 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:
  Infini Band, RDMA, NVLink

• Bachelors, MS/PhD in Computer Science/Engineering or equivalent industry experience

Location:

Austin, TX or Santa Clara, CA strongly preferred; remote is a possibility for the right candidate

This role is not eligible for visa sponsorship.