Head of AI Engineering Productivity, Global Cluster Engineering

Advanced Micro Devices, Inc•Seattle, WA

4d•Hybrid

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. AMD is seeking a visionary Head of AI Engineering Productivity to lead AI enablement and accelerate the adoption of agentic systems across software and hardware engineering workflows. This role sits at the apex of AMD’s cluster innovation, driving step-function agentic improvements in how software is built, hardware is validated, and decisions are made.

Requirements

Deep expertise in AI/ML systems, Large Language Models (LLMs), agentic architectures, modern AI tooling, AI-native developer workflows, AI copilots, autonomous agents, and intelligent workflow systems.
Hands-on experience with agent frameworks, tool use, orchestration systems, memory architectures, evaluation systems, model integration, model evaluation, and model deployment.
Experience delivering platform-level products and production-ready AI solutions within large-scale engineering organizations.
Expertise implementing intelligent automation solutions, including automated bug triage, issue routing, workflow automation, intelligent task execution, AI-powered documentation generation, onboarding assistants, knowledge retrieval systems, and conversational AI.
Experience optimizing inference performance, AI infrastructure, scalability, operational efficiency, and deployment costs at enterprise scale.
Knowledge of multimodal models, emerging AI ecosystems, open-source AI communities, agent technologies, and developer productivity tools.
Proven track record delivering complex technology platforms at scale and driving cross-organizational transformation initiatives across engineering, research, product, infrastructure, and business organizations.
Ability to operate at both strategic and hands-on levels while translating emerging AI capabilities into measurable business impact.
Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, Machine Learning, Software Engineering, or a related technical field; Master’s, PhD degree preferred.

Responsibilities

Define and execute AMD’s AI enablement strategy across software engineering, hardware development, validation, operations, and business functions.
Lead the adoption of AI copilots, autonomous agents, and intelligent workflows throughout the engineering lifecycle.
Deploy agentic systems that autonomously triage issues, route work, and propose solutions across large-scale engineering environments.
Build scalable AI platforms, APIs, frameworks, and services that accelerate safe and effective AI adoption across teams.
Establish best practices for AI model integration, evaluation, deployment, governance, and operationalization.
Develop reusable agent frameworks that support tool orchestration, memory management, workflow automation, and customization.
Identify opportunities to replace manual processes with intelligent, AI-driven workflows that improve efficiency and scalability.
Drive measurable improvements in engineering productivity, developer velocity, hardware validation cycles, and organizational effectiveness.
Enable AI-powered capabilities such as documentation generation, onboarding assistants, knowledge retrieval, and workflow automation.
Transform internal support functions through conversational agents capable of executing tasks across business operations.
Partner with engineering, research, infrastructure, and product leaders to identify priorities and accelerate AI adoption.
Collaborate with external partners, model providers, infrastructure vendors, and open-source communities to incorporate best-in-class technologies.
Optimize AI platform performance, inference efficiency, and operational cost at scale.
Evaluate emerging AI ecosystems, multimodal models, development tools, and agentic technologies to guide strategic investments.