Sr. Fellow, ML Workload Performance

We are seeking an exceptional Senior Fellow in Engineering to serve as a technical leader for Machine Learning workload performance and optimization across frameworks and model architectures. This is a highly visible individual contributor (IC) role with broad technical scope and company-wide impact,operatingat a level equivalent to Senior Director while focusing primarily on deep technical leadership rather than organizational management.

As Senior Fellow of ML workload performance, you will define and drive the technical vision for performance optimization across multiple generations of AMD GPUs, spanning a wide range of models includingstate-of-the-artAI systems. You will lead through influence, setting direction for optimization strategies, guiding architectural decisions, and accelerating support for emerging models.

You will work closely with customers, business units, and engineering teams to develop performance projections, conduct deep analysis, and deliver innovative solutions that meet partner needs. You will also play a key role in industry-standard benchmarks and external technical engagements.

This role is ideal for a hands-on technical leader who is passionate about extracting maximum performance from hardware and shaping the future of AI acceleration.

The ideal candidate is a recognized technical expert with a strongtrack recordof driving large-scale performance optimization initiatives in ML systems. You bring deepexpertisein ML hardware architecture, software optimization, and performance modeling, along with a strong understanding of how model architectures map to low-level software and hardware execution.

You are comfortable operating across the full stack-from model design to kernel-level optimization-and understand the performance implications at each layer. You influencewithout authority, mentor senior engineers, and provide technical direction across multiple teams.

Strong knowledge of modern generative model architectures, includingstate-of-the-artLLMs, diffusion models, and multimodal systems, as well as distributed inference and large-scale deployment, is essential.

• Define and drive the technical strategy and roadmap for ML model optimization across AMD platforms.

• Serve as the highest-level technical authority in performance optimization, guidingarchitecture,and implementation decisions.

• Lead performance tuning, profiling, and analysis of large-scale models (LLMs, diffusion, multimodal,RecSys, generative AI) across single-node and distributed environments. * Drivehardware-softwareco-design initiatives to influence future GPU architectures and system-level optimizations.

• Collaborate across engineering, research, and customer-facing teams to deliver best-in-class performance.

• Develop advanced methodologies, tools, and infrastructure for performance estimation, modeling, and benchmarking.

• Provide technical mentorship to senior engineers and influence best practices across the organization.

• Represent AMD in external technical forums, benchmarks, and customer engagements. * Communicate complex technical findings and recommendations to senior leadership and stakeholders.