
Fellow, EPYC AI Product Architecture
- Austin, TX
- Permanent
- Full-time
- 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 PyTorch, TensorFlow, and inference runtimes like ONNX Runtime or TensorRT
- Understanding of model deployment pipelines, sparsity techniques, advanced numeric formats, and mixed precision
- Optional: CUDA programming or Hugging Face pipelines
- Track record of cross-functional leadership and working in fast-paced, ambiguous environments
- MS or PhD in Computer Engineering, Computer Science, Electrical Engineering, or a related field
- Recognized industry or academic thought leader; publications and patents in AI architecture a strong plus