Product Development Engineer

About This Role

This is a critical role focused on improving the quality and reliability of AMD's industry-leading Server CPUs and Instinct accelerators. The position sits at the intersection of product engineering, test, yield, quality, and data science, involving hands-on work with massive volumes of manufacturing and test data to transform them into actionable insights and production-ready analytics and machine-learning solutions. It suits a curious, analytical problem solver who thrives in complex, data-rich environments and enjoys moving from exploration to deployment, communicating results clearly to both technical teams and executive stakeholders.

Responsibilities

• Partner with Product Engineering, Test Engineering, Yield Engineering, Quality, and Operations to identify, define, and prioritize analytics and machine-learning use cases that materially 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 in-depth root-cause analysis of yield loss and DPPM drivers by correlating test, fab, assembly, and design variables; distinguish systemic issues from statistical noise
• Build, productionize, and maintain yield and quality analytics within AMD’s AVA analytics platform, moving models from experimentation to scalable deployment
• Drive adaptive test, screening, and binning strategies using data-driven insights to optimize product quality and manufacturing efficiency
• Author clear technical documentation and reports; present findings and recommendations to cross-functional teams and executive audiences
• Contribute reusable code, analytics frameworks, and best practices that elevate team capability and accelerate future development

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 (e.g., pandas, NumPy, scikit-learn), general coding experience and demonstrated past 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 ML solutions into production environments, not just develop them in notebooks

Nice to have:

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