As a Senior/Staff Product Development Engineer (SMTS), you will play a critical role in improving yield, quality, and manufacturing efficiency for AMD’s industry‑leading Server CPUs and Instinct accelerators. This role sits at the intersection of product engineering, test, yield, quality, and data science. You will work hands‑on with massive volumes of manufacturing and test data, transforming them into actionable insights and production‑ready analytics and machine‑learning solutions.

You will develop and deploy advanced analytics that accelerate yield learning, reduce DPPM, detect excursions earlier, and optimize test time and cost at scale. This is a highly cross‑functional role where you will partner closely with Product Engineering, Test Engineering, Quality/Reliability, and Operations teams, influencing decisions from early ramp through high‑volume manufacturing. The ideal candidate enjoys moving from exploration to deployment, and communicating results clearly to both technical teams and executive stakeholders.

• 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.

• 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.
• Proficiency in Python and SQL for data analysis and modeling (e.g., pandas, NumPy, scikit‑learn); experience with deep learning frameworks such as PyTorch or TensorFlow is a plus.
• Practical knowledge of semiconductor manufacturing and test flows, including wafer sort and final test, binning strategies, DPPM analysis, reliability screens, and excursion management.
• Familiarity with yield analytics ecosystems similar to AMD’s environment; experience with server‑class CPUs and/or accelerator/GPU products is highly desirable.
• Demonstrated ability to deploy analytics or ML solutions into production environments, not just develop them in notebooks.

ACADEMIC CREDENTIALS:

• Bachelor’s, Master’s, or Doctoral degree in Electrical Engineering, Computer Engineering, Computer Science, or a related technical discipline is preferred.