Fellow, Customer Quality, Data Center & AI Platform

Advanced Micro Devices, Inc•Santa Clara, CA

2d•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. THE ROLE: At AMD, we push the boundaries of innovation to solve the world’s most important challenges. As a Fellow, Customer Quality, you will drive AI infrastructure quality for next-generation data center CPUs and GPUs powering hyperscale and enterprise deployments. This is a high-visibility role within the Customer Engineering organization, focused on ensuring quality outcomes across customer deployments. You will bridge the gap between internal silicon development and real-world customer environments, translating large-scale deployment signals into actionable insights that improve product and platform quality and reliability. Working closely with customers, engineering and the corporate Quality organization, you will drive customer-facing quality metrics, enable closed-loop improvements, and ensure customer-reported issues and system-level behaviors are reflected in product and quality processes. THE PERSON: The ideal candidate is a strategic and technically deep leader with a strong customer-first mindset and a passion for improving quality outcomes at scale. You are highly collaborative and effective at influencing across engineering, manufacturing, and quality organizations. You are comfortable operating in complex, fast-paced environments and working directly with hyperscale and enterprise customers. You bring strong communication skills, the ability to translate technical insights into actionable improvements, and a track record of driving alignment across cross-functional teams.

Requirements

Several years of experience in semiconductor product quality, customer quality, or silicon manufacturing quality
Background in semiconductor product quality, customer quality, or silicon manufacturing quality, including high-volume silicon deployments in hyperscale or enterprise environments
Deep understanding of data center CPUs, GPUs, or AI accelerator platforms, including server system architecture (compute, memory, I/O, and RAS)
Expertise in product reliability methodologies, manufacturing quality systems, and product qualification processes
Demonstrated ability to translate customer and system-level failures into improvements in product design, test coverage, and manufacturing screening
Proven capability in defining test patterns and system-level validation methodologies to detect time-zero and reliability defect modes
Strong foundation in system-level quality and reliability analysis across complex silicon or SoC architectures, including large-scale validation or fleet environments
Track record of influencing cross-functional teams and driving quality improvements across engineering, manufacturing, and quality organizations
Experience collaborating with foundry and OSAT partners to achieve defect-per-million (DPM) targets and improve manufacturing outcomes
Familiarity with advanced silicon technologies, heterogeneous compute, or high-performance computing architectures

Responsibilities

Drive customer quality outcomes across AI and data center infrastructure deployments, ensuring high reliability in production environments
Translate fleet-scale signals and customer issues into actionable improvements across product, validation, and manufacturing
Monitor and improve customer-facing quality metrics across hyperscale and enterprise deployments
Apply advanced reliability methodologies, including physics-based modeling and statistical analysis, to improve quality outcomes
Enable predictive approaches that support high-confidence deployments at scale
Partner with architecture, design, manufacturing, OSAT, and the corporate Quality organization to align on quality priorities
Influence cross-functional decisions to improve product and platform quality outcomes
Support product launch readiness by identifying test gaps and driving improvements in validation and qualification
Provide feedback from customer deployments to improve manufacturing screening, defect detection, and DPM performance
Strengthen feedback loops between customer environments and internal engineering teams