Lead AI Engineer — Semiconductor AI Innovation

Job Summary & Responsibilities

The Lead AI Engineer will drive AI-powered solutions for semiconductor equipment operations. They will work closely with senior tool designers, process engineers, and applications teams to understand complex workflows and data flows. Key responsibilities include defining the AI strategy and architecture, shipping agent assistants, leading predictive maintenance, computer vision for defect detection, and optimizing process yields.

About The Role

This is a hands-on technical leadership role. You will work closely with senior tool designers, process engineers, and applications teams to understand complex workflows and data flows. You'll architect, prototype, and productionize AI solutions that accelerate innovation, improve yields, and reduce tool downtime.

Minimum Qualifications

• 5+ years applied ML/AI experience, with 3+ years in a technical leadership role.
• Hands-on expertise with at least two of the following domains:
• Large Language Models - RAG, fine-tuning, agent frameworks, prompt optimization.
• Predictive Modeling - tool failure prediction, anomaly detection, time-series analysis.
• Computer Vision - defect detection, segmentation, or SEM/optical imaging.
• Advanced Python proficiency: C++/CUDA familiarity is a plus.
• Experience with MLOps stacks: containers, CI/CD, Ray Serve/Triton, model registries (e.g., MLflow), and GPU optimization.
• Strong stakeholder collaboration skills and the ability to translate between engineering, operations, and leadership.
• Demonstrated success delivering AI-powered products into production.

Nice-to-Haves

• Familiarity with semiconductor manufacturing, inspection, or metrology.
• Understanding of fab interfaces and data connectivity (SECS/GEM, GEM300).
• Prior experience deploying digital twins or simulation-driven optimization.
• Knowledge of vector databases, retrieval pipelines, and hybrid search.
• Experience implementing safety, security, and IP protections for AI systems.
• Exposure to datasets or tools from KLA, ASML, Applied Materials, Onto, Nova, or similar inspection/metrology vendors.