Product Quality Engineering

Advanced Micro Devices, Inc•Austin, TX

About The Position

As a senior Product Quality Engineer, you will improve the quality and reliability of AMD Server CPUs and Instinct accelerators. The role combines quality and reliability modeling, product engineering, test, yield, and data science. You will work with large volumes of manufacturing and test data to develop actionable insights and production-ready analytics and machine-learning solutions. You will define and deploy scalable methods and tools for per-part reliability modeling, outgoing-quality prediction, DPPM reduction, early excursion detection, and test-time and cost optimization. The role provides technical direction across Product Engineering, Test Engineering, Quality/Reliability, Yield Engineering, foundry, and Operations teams, influencing decisions from technology development and early ramp through high-volume manufacturing across multiple product families. You will take work from exploration through deployment and communicate results to technical teams and executive stakeholders.

Requirements

Strong background in semiconductor product development, yield engineering, or test engineering, with hands-on experience analyzing high-volume manufacturing data.
Solid foundations in statistics and machine learning, including hypothesis testing, DOE/RSM, regression, classification, outlier detection, time-series analysis, and anomaly detection.
Familiarity with Python and SQL for data analysis and modeling, including tools such as pandas, NumPy, and scikit-learn; general coding experience and demonstrated ability in related scientific computing work.
Practical knowledge of semiconductor manufacturing and test flows, including wafer sort and final test, binning strategies, DPPM analysis, reliability screens, and excursion management.
Demonstrated ability to deploy analytics or machine-learning solutions into production environments, not just develop them in notebooks.
Relevant industry experience, or a Master’s or Doctoral degree in Electrical Engineering, Computer Engineering, Computer Science, Physics, Applied Math, or a related technical discipline.

Nice To Haves

Familiarity with yield analytics ecosystems similar to AMD’s environment.
Experience with server-class CPUs and/or accelerator/GPU products.

Responsibilities

Set technical direction for reliability and quality analytics across AMD Server CPU and Instinct product families, establishing scalable methods and influencing product, test, manufacturing, and quality decisions.
Partner with the foundry technology team to characterize, model, and improve reliability throughout technology development.
Develop pre-silicon product reliability assessments.
Establish scalable methods and frameworks for reliability modeling, outgoing-quality prediction, DPPM analytics, screening optimization, and excursion detection.
Partner with Product Engineering, Test Engineering, Yield Engineering, Quality, and Operations to identify, define, and prioritize analytics and machine-learning use cases that improve yield, quality, and cost.
Develop and deploy ML/AI solutions for parametric outlier screening, drift detection, and excursion/anomaly detection across wafer, lot, site, and package dimensions.
Perform root-cause analysis of yield loss and DPPM drivers by correlating test, fab, assembly, and design variables, and distinguish systemic issues from statistical noise.
Translate reliability and quality insights into test, screening, process, or product changes, and validate that corrective actions resolve targeted failure modes and improve outgoing quality.
Build, productionize, and maintain yield and quality analytics within AMD’s AVA analytics platform, deploying models at scale.
Drive adaptive test, screening, and binning strategies using data-driven insights to optimize product quality and manufacturing efficiency.
Write technical documentation and reports, and present findings and recommendations to cross-functional teams and executive audiences.
Contribute reusable code, analytics frameworks, and best practices to strengthen team capability and accelerate future development.