AI Engineering Architect – Agentic AI & Digital Engineering

KLA•Milpitas, CA

About The Position

KLA is a global leader in diversified electronics for the semiconductor manufacturing ecosystem. Virtually every electronic device in the world is produced using our technologies. No laptop, smartphone, wearable device, voice-controlled gadget, flexible screen, VR device or smart car would have made it into your hands without us. KLA invents systems and solutions for the manufacturing of wafers and reticles, integrated circuits, packaging, printed circuit boards and flat panel displays. The innovative ideas and devices that are advancing humanity all begin with inspiration, research and development. KLA focuses more than average on innovation and we invest 15% of sales back into R&D. Our expert teams of physicists, engineers, data scientists and problem-solvers work together with the world’s leading technology providers to accelerate the delivery of tomorrow’s electronic devices. Life here is exciting and our teams thrive on tackling really hard problems. There is never a dull moment with us. With over 40 years of semiconductor process control experience, chipmakers around the globe rely on KLA to ensure that their fabs ramp next-generation devices to volume production quickly and cost-effectively. Enabling the movement towards advanced chip design, KLA's Global Products Group (GPG), which is responsible for creating all of KLA’s metrology and inspection products, is looking for the best and the brightest research scientist, software engineers, application development engineers, and senior product technology process engineers. The RAPID division is the world leading provider of reticle inspection solutions for the semiconductor industry. The company provides inspection solutions to both the mask shops and the semiconductor fabs to ensure that lithography yields are consistently high thus enabling cost-effective manufacturing.

Requirements

Master’s degree or PhD in Computer Science, AI/ML, Data Science, Electrical Engineering, Mechanical Engineering, Physics, Systems Engineering, or a related field.
Strong hands-on experience building and deploying AI/ML solutions for complex engineering or industrial workflows.
Experience with integrating AI into engineering data, software, or workflow systems.
Experience with engineering data such as sensor streams, logs, structured/unstructured technical documents, or time-series datasets.
Ability to translate ambiguous engineering pain points into scalable AI architectures and deployable solutions.
Doctorate (Academic) Degree and related work experience of 5 years; Master's Level Degree and related work experience of 8 years; Bachelor's Level Degree and related work experience of 12 years

Nice To Haves

Experience with agentic AI, tool-using LLM workflows, multi-agent systems, or RAG/vector-backed knowledge systems.
Experience with time-series forecasting, anomaly detection, or predictive analytics for industrial systems.
Experience with MBSE, digital thread, SysML, requirements traceability, or model-centric engineering.
Experience with CAD/PLM/PDM integration, BOM workflows, or engineering data interoperability.
Experience in semiconductor equipment, capital equipment, optics, plasma, vacuum systems, or manufacturing engineering.

Responsibilities

Own the AI roadmap for EUV source and digital engineering use cases; identify, prioritize, and sequence high-value opportunities tied to engineering impact.
Build AI solutions for source health analytics, anomaly detection, and predictive monitoring using engineering telemetry, metrology, and operational data.
Develop AI-assisted and agentic workflows for alarm audit, root-cause analysis, procedure creation, and engineering task automation.
Partner with engineering teams to enable AI-assisted modeling, digital thread, MBSE, and knowledge-grounded engineering workflows.
Support AI-assisted routing, CAD integration, model-to-BOM, and early BOM activities as part of engineering digital transformation.
Define AI observability, evaluation, tracing, and safe production deployment practices for engineering AI systems.