Forward Deployed Engineer

Company Overview

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.

This role sits at the intersection of KLA’s most advanced AI work and the rest of the company. Based in Ann Arbor, you are the bridge between what our AI engineering team builds and what KLA’s business units need — identifying which capabilities are ready to deploy and building the infrastructure that every future AI deployment stands on. You own the internal AI platform that raises the capability floor across the organization.

• Build and enhance AI products and tools for the business, owning product quality and reliability end to end.
• Deploy AI capabilities into KLA’s teams, serving as the bridge between R&D and production use.
• Own the internal AI infrastructure — the foundational layer that removes friction, compounds organizational learning, and scales without adding headcount.
• Architect and maintain reusable infrastructure: agent scaffolding, eval frameworks, and integration patterns.
• Design and build persistent context and memory systems that make AI environments personalized and progressively more useful over time.
• Build skill and plugin distribution systems that let one person’s breakthrough become everyone’s baseline.
• Design and operate on‑prem inference infrastructure where data cannot leave the network.
• Fine‑tune or adapt foundation models on proprietary data to create meaningful capability uplift.
• Build CI/CD pipelines for agents: testing, evaluation, versioning, and rollback.
• Set technical standards and mentor junior engineers; lead design reviews and define golden paths for development.
• Track the AI frontier at a deep technical level — not just what shipped but how it works and what it changes about what's possible.

• Bachelor’s degree in Computer Science, Software Engineering, or related field;
  Master’s preferred.
• 3+ years of experience building and operating ML/AI systems in production, including ownership of production‑grade infrastructure.
• Strong proficiency in Python and experience with ML frameworks and model fine‑tuning techniques.
• Hands‑on experience with inference optimization and model serving infrastructure, including on‑prem deployment patterns.
• Experience building internal tools, platform infrastructure, or developer experience layers that other engineers or non‑technical users depend on.
• Experience designing plugin architectures, marketplace systems, or extensible platform components that scale adoption.
• Experience building persistent memory, context management, or personalization systems for AI products.
• Working knowledge of CI/CD, containerization, orchestration, and Infrastructure‑as‑Code.
• Experience designing evaluation harnesses and automated testing for AI systems.
• Strong understanding of data governance and security — comfortable operating within tiered data handling policies.
• Owns outcomes, not tasks — you identify what needs to happen and drive it without being managed.
• Technical leadership experience: mentoring engineers, leading design reviews, setting standards.
• Prior experience in semiconductor, manufacturing, or scientific/industrial ML is a plus.

Doctorate (Academic) Degree and 0 years…