Product Line Quality Engineer

Advanced Micro Devices, Inc•San Jose, CA

2h•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 Product Line Quality (PLQ) Engineer serves as the cross‑functional leader and end‑to‑end owner of product quality at AMD, from early concept through production and field use. This role drives alignment across design, test, reliability, manufacturing, supply chain, and customers, applying sound technical judgment to identify risk, balance quality with cost and schedule, and translate complex data into clear, executive‑level decisions.

Requirements

Technically strong, self‑driven engineer with deep expertise in semiconductor product quality, failure analysis, and reliability engineering.
Hands‑on experience with silicon, package, system, and field debug; test and screening methodologies, and data‑driven quality analysis such as DPPM, yield, and risk modeling.
An effective communicator with strong attention to detail.
Works independently, takes ownership of complex technical challenges, and proactively drives problems to closure while continuously seeking process and product improvements.
Intermediate programming skills either with object-oriented or scripting languages.

Nice To Haves

Experience working in semi-conductor or electronics in either quality, reliability, product development engineering role.
Experience with application hardware design, semiconductor packaging, FPGA, CPU/GPU or ARM technology, and markets such as datacenter, automotive and aerospace.
Exceptional and well-developed time and project management skills.
Knowledge on adopting and applying Artificial Intelligence methods and practices into work flows. Training on Artificial Intelligence is an advantage.
Excel technical and executive-level presentations and communication skills using modern tools like Powerpoint, PowerBI, charts and graphs etc.
Demonstrate strong statistical data analysis acumen. Proficiency in Mini-Tab or JMP is desired.
Strong understanding of component- and system-level test methodologies as well as failure analysis techniques for silicon components.
Masters of Science in Applied Sciences/Material Science/Electrical Engineering or related field preferred.

Responsibilities

Own quality strategy and execution across the full product lifecycle—from pre-concept and NPI through production and field support.
Drive silicon, package, system, and field failure analysis driving cross‑functional root cause, containment, and closure.
Partner with Packaging and Test Engineering to ensure robust test coverage, effective screening, and data‑driven guard‑banding based on risk and DPPM trends.
Define and drive product qualification, FMEA/DFMEA risk mitigation, and reliability‑based life modeling.
Perform DPPM modeling, yield analysis, DOE, and cross‑domain data correlation to support product‑gate and executive decisions.
Lead manufacturing, supplier, and component quality initiatives, change management, supplier qualification, and quality governance.
Interface between customer facing Customer Quality Engineering and the internal manufacturing team.
Drive system‑ and platform‑level quality readiness, integrating silicon, validation, and field data to mitigate scaling and interaction risks.
Build dashboards and executive reports using modern analytics tools; apply advanced screening and outlier‑detection techniques to reduce escapes.
Translate complex technical data into clear executive narratives, influence cross‑functional teams without authority, and balance quality, cost, and time‑to‑market.