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. Backed by Sequoia.
About the role
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 standards), sex (including pregnancy, childbirth or related medical conditions, breastfeeding or related medical conditions), gender (including a person’s gender identity, gender expression, and gender-related appearance and behavior, whether or not stereotypically associated with the...
What they're looking for
- 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
More about this role
- 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 —...
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