Staff/Principal Engineering Lead, TD RDA

About this role

Required Responsibilities
• Own the site-level RDA strategy and annual roadmap aligned to NAND TD technology nodes.
• Lead analysis of experimental and production flows to detect, classify, and trend systematic and random defects.
• Establish, track, and report program health metrics (baseline health, detection rates, false positives, MTTR).
• Direct fast containment for defect excursions: trigger tool quarantines, define hold criteria, and lead 8D corrective actions.
• Architect inspection recipes and sampling strategies; optimize sensitivity, throughput, and cost of ownership.
• Partner with Process Integration, Yield, and Data Science to correlate inspection signals to device performance and yield.
• Influence supplier roadmaps; evaluate, qualify, and release next‑gen optical/e-beam inspection capabilities.
• Standardize and disseminate Best Known Methods (BKM) across global TD and manufacturing sites.
• Mentor, coach, and upskill engineers; develop succession plans and technical ladders within the RDA domain.
• Champion safety and continuous improvement (CIP); drive kaizen/lean initiatives that improve cycle time and learning velocity.
- Drive and enable an aggressive AI roadmap to enhance value of every engineer in the group
Key Responsibilities
• Serve as the technical lead for RDA methodologies and execution within NAND TD.
• Perform root cause analysis using statistical, image-based, and data-driven techniques.
• Develop, own, and communicate inspection and defect detection technology roadmaps.
• Define program health metrics and drive continuous improvement initiatives (CIP).
• Lead tool anomaly investigations, quarantine decisions, and corrective action planning.

• Embrace and enable innovations associated with AI to enable best in class insight generation while championing efficiency gains

• Collaborate globally to align Best Known Methods (BKM) across co-location sites pre/post transfer as well as across worldwide sites.
Stakeholder Collaboration
• Technology Development (TD) Process and Integration teams
• Yield Analysis and Data Science organizations
• Global Manufacturing and Operations
• Inspection/metrology equipment suppliers
• Program and engineering leadership across regions
Required Qualifications
• Bachelor’s degree with 7+ years relevant semiconductor experience, or Master’s/PhD with 5+ years.
• Minimum 3+ years hands-on RDA experience in semiconductor manufacturing or equipment engineering.
• Demonstrated expertise in defect inspection, process monitoring, and yield learning.
Technical Competencies
• Realtime Defect Analysis (RDA) systems and workflows.
• Optical and e-beam inspection technologies.
• Statistical data analysis, correlation development, and large dataset interpretation.
• Semiconductor process integration and technology development.
• Root cause analysis and systematic problem solving (e.g., 8D, DMAIC).
Leadership & Behavioral Competencies
• Recognized technical authority with the ability to influence with and without direct authority.
• Strong mentoring and coaching mindset with proven talent development outcomes.
• Clear, concise communication skills across technical and executive audiences.
• Ownership-driven, execution-focused; comfortable operating in ambiguity.
Impact & Success Measures
• Reduced defect-related yield loss and faster learning cycles.
• On-time qualification and adoption of next-generation inspection capabilities.
• Uplift in organizational RDA skill depth and succession pipeline.
• Alignment of inspection strategy and capability to future NAND nodes.
AI Responsibilities:
• Integrates AI-assisted tools and insights into daily work to improve efficiency, quality, or effectiveness, exercising sound judgment and complying with organizational standards and legal requirements.
• Contributes to a culture of continuous improvement by identifying, testing, and sharing AI-enabled enhancements within one’s scope of work.
AI Requirements:
• Ability to apply baseline digital fluency and role‑appropriate AI literacy to use AI‑enabled tools responsibly and effectively for research, analysis, content creation, problem‑solving, operational tasks, and achieving business outcomes