Diagnostics Software Engineer (AI/ML)

Responsibilities

• Design, develop, test, and maintain LabVIEW-based and multi-language software for diagnosing and calibrating KLA's reticle inspection platforms.
• Learn the existing diagnostics and calibration software architecture and contribute clean, maintainable, well-tested code to meet program goals.
• Translate system-level and user requirements into software designs, implementations, and test plans.
• Explore and apply AI/ML and generative-AI techniques — LLMs, agents, copilots, and automation — to build applications and internal tools that improve diagnostics, calibration, developer productivity, and data analysis.
• Rapidly prototype AI-driven proofs-of-concept, evaluate feasibility and impact, and mature promising ideas into production-ready tools.
• Improve software reliability, maintainability, performance, and scalability through sound design and refactoring.
• Build, maintain, and improve CI/CD pipelines and workflows to support reliable, automated build, test, and release of diagnostics & calibration software.
• Participate actively in design reviews, code reviews, and development best practices.
• Partner cross-functionally with hardware, subsystem, system, manufacturing, and service teams to deliver integrated solutions.
• Support root-cause analysis of complex system-level issues and drive effective, sustainable solutions.
• Travel occasionally to global sites and customer locations to support integration, bring-up, and deployment activities (up to ~10%).
• Minimum Qualifications
• Bachelor's or Master's degree in Computer Science, Physical Science, Electrical/Electronics Engineering, Optics, Microscopy, or a related field.
• 4+ years of software development experience (or 2+ years with a Master's) building medium to large-scale applications in multi-developer environments.
• Proficiency with object-oriented design and experience with one or more additional languages such as Python, C++, or C#.
• Genuine enthusiasm for exploring AI/ML and generative-AI tools, with a demonstrated drive to build working applications with them; hands-on experience building applications with LLMs / generative AI — fine tuning, RAG, or agentic frameworks, or integrating AI APIs/copilots into engineering workflows - personal projects, prototypes, hackathons, or on-the-job tooling all count.