This position combines complex data analysis of various IC parametric measurements, close work with internal groups from various fields (Build, backend, layout and analog teams, Operations, IC test, Reliability, Characterization etc.) and close interaction with the foundry engineering teams.
This position suits someone passionate about semiconductor manufacturing, yield optimization, and working with world-class teams to deliver flawless performance.
What you'll be doing:
Drive yield and performance improvements across advanced nodes.
Serve as the key interface between us and foundry partners, leading technical communication and driving issues to full containment.
Collaborate in a matrix structure and multiple domains for comprehensive product optimization.
Develop and apply data pipelines to identify yield limiters and process excursions.
Build automated monitoring and alerting frameworks to proactively detect process drift.
What we need to see:
B.Sc. in Electrical Engineering, Materials, Physics, Chemical Engineering, or equivalent experience.
5+ years of proven experience in Process, Integration, Device engineering, or Semiconductor manufacturing.
Strong knowledge of FAB processes, integration flows, device behaviour, and quality metrics.
Outstanding problem-solving and data analysis skills.
Experience with large datasets and correlation methodologies.
Ability to multitask, prioritize, and complete tasks under pressure.
Strong communication and presentation skills.
High ownership, autonomy, and accountability.
Familiarity with scripting/programming (Python or similar) for data analysis and workflow automation.
Passion for data systems, automation, and applying AI/ML techniques to engineering problems.
Ways to stand out from the crowd:
Experience working directly with FAB or Silicon technology supporting systems (inspection tools, OSAT, etc.).
Hands-on experience with data analysis tools (e.g., JMP).
Experience with visualization tools (e.g., Tableau, custom Python dashboards).
Experience building or using ML models for yield prediction, defect classification, or process control.
Familiarity with data platforms or semiconductor-specific data ecosystems.
