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THE ROLE:
As a senior Product Quality Engineer, you will play a critical role in improving quality and reliability of AMD’s industry-leading Server CPUs and Instinct accelerators. This role sits at the intersection of quality and reliability modeling, product engineering, test, yield, 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 define and deploy scalable methodologies and tools for per-part reliability modeling, outgoing-quality prediction, DPPM (defective parts per million) reduction, early excursion detection, and test-time and cost optimization. This is a highly cross-functional role where you will provide 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. The ideal candidate enjoys moving from exploration to deployment and communicating results clearly to both technical teams and executive stakeholders.
THE PERSON:
You are a curious, analytical problem solver who thrives in complex, data-rich environments. You enjoy uncovering patterns in noisy data and translating them into practical improvements that matter in production. You collaborate naturally across functions, influence without authority, and are comfortable balancing deep technical work with clear communication. You are equally effective working independently or as part of a fast-paced, cross-disciplinary team, and you take pride in building robust, reusable solutions that scale.
KEY 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 at all stages of technology development.
- Develop pre-silicon product reliability assessments.
- Define and establish scalable methodologies and frameworks for reliability modeling, outgoing-quality prediction, DPPM analytics, screening optimization, and excursion detection, enabling reuse across products and engineering teams.
- 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.
- Drive closed-loop corrective action by translating reliability and quality insights into test, screening, process, or product changes and validating that those actions measurably resolve the targeted failure modes and improve outgoing quality.
- 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.
PREFERRED EXPERIENCE:
- 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 (defective parts per million) 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:
- Relevant industry experience, or 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
Benefits offered are described: AMD benefits at a glance.
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.
This posting is for an existing vacancy.