Yield Development Engineering Manager-strategic Programs

Intel Intel · Semiconductors · Oregon, Hillsboro, United States

Seeking a Senior Strategic Program Leader to drive Yield and Defect Engineering transformation in semiconductor manufacturing, focusing on AI-driven innovation, enterprise-scale program execution, and embedding AI/ML into daily operations for predictive and adaptive capabilities. The role involves leading a multidisciplinary organization to advance defect metrology, yield engineering systems, and AI-enabled decision intelligence, defining strategy, influencing roadmaps, and driving execution of complex programs.

What you'd actually do

  1. Define and execute a multi-year strategy for Yield and Defect Engineering, integrating AI/ML, automation, and digital transformation into core workflows.
  2. Champion the deployment of AI/Agentic AI solutions to enhance defect detection, classification, excursion prevention, and root cause analysis.
  3. Oversee yield performance across development and high-volume manufacturing, ensuring alignment to aggressive yield, quality, and delivery targets.
  4. Define and evolve defect metrology strategy, including tool selection, capability roadmaps, and system architecture.
  5. Build and lead a high-performing, diverse organization, fostering a culture of innovation, accountability, and continuous learning.

Skills

Required

  • Bachelor's degree in Engineering, Physics, Materials Science, Data Science, or related field
  • 12+ years of experience in semiconductor manufacturing with deep expertise in yield engineering, defect metrology, or process integration.

Nice to have

  • Advanced degree (MS/PhD) in a relevant technical or data-centric discipline.
  • Experience leading AI/ML transformation initiatives in semiconductor or adjacent high-tech industries.
  • Familiarity with digital manufacturing ecosystems (data platforms, MES integration, advanced analytics tools).
  • Track record of defining and scaling enterprise-level engineering systems or platforms.
  • Strong business acumen with ability to connect technical outcomes to cost, cycle time, and revenue impact.
  • Experience in advanced packaging technologies (e.g., Foveros, EMIB, heterogeneous integration) is a plus.
  • Proven track record leading complex, cross-functional programs in high-volume manufacturing environments.
  • Demonstrated experience driving yield improvement and defect reduction at scale, including ramp and HVM phases.
  • Strong domain expertise in defect metrology tools, inspection systems, and yield analysis methodologies.
  • Experience integrating data analytics, machine learning, or AI solutions into engineering workflows.
  • Exceptional leadership and communication skills with ability to influence across organizational boundaries.

What the JD emphasized

  • AI-driven innovation
  • enterprise scale program execution
  • AI-enabled decision intelligence
  • predictive and prescriptive yield management systems
  • AI adoption for semiconductor manufacturing
  • deployment of AI/Agentic AI solutions
  • integration of data platforms, digital twins, and advanced analytics
  • embedding real-time decision intelligence into fab operations
  • institutionalize closed-loop learning systems
  • integrating data analytics, machine learning, or AI solutions into engineering workflows
  • Experience leading AI/ML transformation initiatives in semiconductor or adjacent high-tech industries.
  • Track record of defining and scaling enterprise-level engineering systems or platforms.
  • Proven track record leading complex, cross-functional programs in high-volume manufacturing environments.
  • Demonstrated experience driving yield improvement and defect reduction at scale, including ramp and HVM phases.

Other signals

  • AI-driven innovation
  • enterprise scale program execution
  • AI-enabled decision intelligence
  • predictive and prescriptive yield management systems
  • AI adoption for semiconductor manufacturing
  • deployment of AI/Agentic AI solutions
  • integration of data platforms, digital twins, and advanced analytics
  • embedding real-time decision intelligence into fab operations
  • institutionalize closed-loop learning systems
  • integrating data analytics, machine learning, or AI solutions into engineering workflows