Product Development Engineer
About The Position
This critical role focuses on improving the quality and reliability of AMD server CPUs and Instinct accelerators. It sits at the intersection of product engineering, test, yield, quality, and data science, using large volumes of manufacturing and test data to develop actionable insights and production-ready analytics and machine-learning solutions. The engineer will develop, deploy, and use tools to analyze test and reliability data, reduce DPPM, detect manufacturing excursions earlier, and optimize test time and cost at scale. The role involves close collaboration with Product Engineering, Test Engineering, Quality/Reliability, and Operations teams, influencing decisions from early product ramp through high-volume manufacturing. The position requires communicating findings clearly 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, 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, beyond developing them in notebooks.
- Bachelor’s, Master’s, or doctoral degree in Electrical Engineering, Computer Engineering, Computer Science, Physics, Applied Math, or a related technical discipline is preferred.
- This role is not eligible for visa sponsorship.
Nice To Haves
- Familiarity with yield analytics ecosystems similar to AMD’s environment.
- Experience with server-class CPUs and/or accelerator/GPU products.
Responsibilities
- 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 or anomaly detection across wafer, lot, site, and package dimensions.
- Conduct root-cause analysis of yield loss and DPPM drivers by correlating test, fab, assembly, and design variables, and distinguish systemic issues from statistical noise.
- Build, productionize, and maintain yield and quality analytics within AMD’s AVA analytics platform, taking 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 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 capabilities and accelerate future development.
