Senior Software Engineer

Onto Innovation designs and manufactures the inspection and metrology tools that the world's leading chipmakers rely on. We believe AI will be a key enabler of faster, more reliable measurement and decision-making in semiconductor manufacturing.

This is a hands-on engineering role. You will design, build, and ship agentic applications for semiconductor metrology, working closely with system engineers, algorithm developers, applications engineers, and software quality engineers throughout the product life cycle to turn complex metrology workflows and data flows into reliable software. You will own features end to end, from design through release and support, and bring senior-level judgment to architecture, quality, and the engineers around you.

• Build agentic applications for metrology, taking them from design through production release.
• Connect agents to real systems. Integrate them with tools, data sources, and internal services through well-defined interfaces.
• Build the application around the agent: the services, data flows, and user interfaces that engineers use every day.
• Engineer for reliability. Automated testing, evaluation, CI/CD, and monitoring for software whose behavior is partly probabilistic.
• Build quality in. Improve software quality through design and code reviews, unit testing, test automation in CI/CD pipelines, refactoring of legacy code, and static and dynamic analysis.
• Deploy in fab conditions, including customer environments with strict performance, security, and data-control requirements.
• Reason across the system. Work with system engineers to translate a metrology problem into well-scoped engineering work, and move between architecture and implementation details as needed.
• Use AI coding tools well, to move faster while keeping ownership of design integrity and code quality. Apply them responsibly to coding, debugging, refactoring, documentation, and test generation, and maintain secure, reliable workflows for evaluating AI-generated code: human review, automated testing, static analysis, security scanning, and validation against requirements.
• Raise the bar for the team. Run design and code reviews, document your decisions, and mentor less experienced engineers. Spot gaps and inefficiencies in software processes and tool chains, and drive improvements.

• 5+ years of professional software engineering, including 2+ years building applications with LLMs or agents that reached production.
• Proficiency in Python, C++, or C#, and a strong grasp of data structures, design patterns, and object-oriented design. Solid software design: APIs, services, concurrency, and message-based or event-driven systems.
• Hands-on experience with LLM application engineering: tool calling, retrieval, prompt and context design, and evaluation.
• Solid engineering practice: automated testing, code review, CI/CD, containers, and version control, with agile and Dev Ops ways of working.
• Hands-on experience with AI-assisted development tools: writing clear prompts, critically reviewing generated code for errors and incorrect assumptions, and turning it into maintainable production code.
• A working understanding of software security, intellectual property, data privacy, and responsible AI when using AI in enterprise software.
• Strong analytical and troubleshooting skills for complex software systems, and the ability to research and solve hard technical problems independently.
• Comfort moving between levels of abstraction, from system architecture to implementation details.
• Experience developing software on Windows with Visual Studio and Azure Dev Ops.
• BS in computer science, software or electrical engineering, physics, applied mathematics, or a related field; MS or PhD preferred.
• Clear written and verbal communication with both engineers and non-specialists, and a genuine commitment to delivering quality software to customers.

• Semiconductor metrology, particularly optical metrology such as scatterometry or optical critical dimension (OCD) software, inspection, or machine vision experience, or time spent in a fab.
• Physics-based modeling, numerical simulation, or scientific computing, and image processing or numerical methods on real measurement data.
• Machine learning experience: model training and evaluation, or applying ML to engineering and scientific problems.
• Experience in integrating AI into the software development process, such as AI-assisted code review, automated test…