Lead Wafer QA Engineer - AI-Driven Quality & Automation

WD; San Jose (CA); On-site; USD 103,000 - 137,000; Full time

Job summary

WD is seeking a Quality Assurance Engineer to advance AI-enabled quality monitoring and analysis for wafer manufacturing. The role emphasizes analyzing data, leading CAPA actions, and integrating FMEA practices to prevent defects and improve yield.

Collaboration across process, equipment, and data teams is essential. The candidate will apply AI/ML concepts to automate surveillance, support MRR/PDR reviews, and drive digital transformation in a fast-paced semiconductor environment.

Qualifications

• Bachelor's degree in Engineering, Materials 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

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.

Skills

Problem-solving

Data analysis

AI/ML experience

FMEA expertise

Quality engineering

Statistical interpretation

Cross-functional collaboration

Attention to detail

Education

Bachelor's degree in Engineering, Materials Science, Physics, Data Sciences or related field

Job description

WD is seeking a Quality Assurance Engineer to advance AI-enabled quality monitoring and analysis for wafer manufacturing. The role emphasizes analyzing data, leading CAPA actions, and integrating FMEA practices to prevent defects and improve yield.

Collaboration across process, equipment, and data teams is essential. The candidate will apply AI/ML concepts to automate surveillance, support MRR/PDR reviews, and drive digital transformation in a fast-paced semiconductor environment.