Job at a glance
Company Description WD is building the infrastructure behind the AI-driven data economy. As AI scales, so does data. Every interaction, every model, every system generates data that must be stored, managed, and made accessible over time. That’s where we come in. We combine deep engineering expertise with global-scale manufacturing to deliver the storage systems that make AI possible, powering hyperscale data centers, cloud platforms, and enterprise infrastructure worldwide.
This isn’t theoretical work. It’s real systems, at real scale, people solving some of the hardest challenges in technology today. We’re looking for people who want to build, solve, and operate at that level. Join us and let’s shape the future of data. Job Description ESSENTIAL DUTIES AND RESPONSIBILITIES Perform wafer paper failure analysis (FA) and provide disposition for issues flagged by customers.
Facilitate wafer manufacturing readiness review (MRR) and process design readiness (PDR) review meetings. Drive 8D closure with corrective action and preventive action (CAPA) implementations to prevent reoccurrence  Failure Mode & Effects Analysis (FMEA): Lead and facilitate FMEA activities across wafer fab to proactively identify, assess, and mitigate potential failure modes and their impact on quality and reliability.
Partner with process, equipment, manufacturing, and data engineering teams to implement automated quality surveillance and anomaly detection solutions. Drive digitalization initiatives that improve CAPA effectiveness, root cause identification, and risk assessment processes. Evaluate and integrate generative AI and digital agent technologies to enhance engineering productivity, knowledge management, and quality decision-making.
Drive adoption of AI-powered quality engineering tools to increase engineering productivity, accelerate defect detection cycle times, and transition the organization from reactive quality management to predictive quality — anticipating and preventing excursions before they impact yield or customer deliverables. Participate in developing and deploying AI-assisted quality monitoring solutions for Change Review Board (CRB), Material Review Board (MRB), Failure Analysis (FA), and FMEA activities.
Qualifications REQUIRED Bachelor's degree in Engineering, Material Science, Physics, Data Sciences or a related technical field (Master's preferred) 3+ years of experience in quality assurance, quality engineering, or a related role within semiconductor or wafer manufacturing Strong problem-solving and root-cause-analysis skills. Ability to analyze datasets and interpret statistical/model-performance results.
Experience working with AI/ML or generative AI systems a plus. Familiarity with FMEA methodology and its application to IT and manufacturing systems Basic understanding of wafer product, including HAMR and knowledgeable of SPC, wafer process build, interconnection between process, equipment, manufacturing, backend a plus Strong cross-functional collaboration skills with the ability to engage both technical and operations teams Excellent analytical and problem-solving skills with a detail-oriented mindset ADDITIONAL SKILLS Experience with end-to-end quality management in semiconductor manufacturing Experience developing or utilizing AI-enabled quality monitoring and reporting systems.
Knowledge of Generative AI, Large Language Models (LLMs), Copilot technologies, or Digital Agents for engineering workflows. Strong data storytelling and visualization skills to communicate complex technical findings to engineering and management teams. Continuous improvement mindset with the ability to identify opportunities for automation and digital transformation.   Additional Information WD is committed to providing equal opportunities to all applicants and employees and will not discriminate against any applicant or employee based on their race, color, ancestry, religion (including religious dress and grooming standar