Fellow, EPYC AI Product Architecture

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.

AMD is seeking a Fellow of EPYC AI Product Architecture to lead the definition of next‑generation CPU and platform innovations tailored for AI workloads. You will be a key technical leader within the EPYC AI Product organization, shaping AMD’s AI platform strategy across silicon, systems, and software. This role sits at the intersection of architecture, product definition, customer engagement, and business impact, driving differentiated solutions across cloud, enterprise, and hyperscale deployments.

You will collaborate with world‑class engineers, technologists, and customers to deliver high‑performance, efficient, and scalable platforms for deep learning, generative AI, recommendation systems, and classical ML. You will engage deeply with AMD’s silicon, platform, and software teams to translate workload insights into architectural innovations and platform capabilities that shape the future of AI compute.

We are looking for a visionary, results‑driven technical leader with a deep understanding of AI workload requirements and system‑level architecture. You combine technical breadth across CPUs, servers, and AI acceleration platforms with customer fluency and strategic business insight
. You are equally comfortable engaging in low‑level performance modeling as you are briefing customers, analysts, and press on AMD’s roadmap and product direction.

Key Attributes:

• Deep technical expertise in CPU and server architecture for AI workloads
• Proven track record influencing AI platform design at the pod, rack, or datacenter scale
• Strong understanding of AI software ecosystems
  , frameworks, and optimization flows
• Data‑driven mindset, with ability to analyze and forecast workload performance across complex systems
• Exceptional communicator who can translate technical complexity into compelling product narratives

• Lead architecture definition for AMD EPYC CPU and server platforms optimized for AI training and inference
• Engage with hyperscalers, OEMs, and AI ISVs to align platform features with evolving workload needs
• Evaluate and drive new CPU and platform features for deep learning models, including generative AI, vision, and recommender systems
• Analyze performance bottlenecks using architecture simulation and hardware instrumentation; propose workload‑driven improvements
• Drive architectural trade‑off analyses across compute, memory, I/O, and network subsystems
• Build and refine performance models, automation tools, and workload testbeds for end‑to‑end analysis
• Project and compare performance vs TCO tradeoffs under different system and silicon configurations
• Shape AMD’s platform strategy for heterogeneous compute
  , working closely with GPU and AI accelerator teams
• Represent AMD in industry forums
  , customer briefings, analyst interactions, and press engagements

• 10+ years in high‑performance CPU, server, or AI platform architecture
  , ideally with customer‑facing responsibilities
• Expertise in AI system deployments at scale (cloud, enterprise, HPC, or edge)
• Demonstrated thought leadership in Generative AI (LLMs), vision, or recommender systems
• Hands‑on experience with performance tools
  , roofline models
  , and system simulation
• Familiarity with AI compilers, quantization flows (QAT/PTQ), and workload optimization techniques
• Proficient in deep learning frameworks such as Py Torch ,
  Tensor Flow
  , and inference runtimes like ONNX Runtime or TensorRT
• Understanding of mo…