Job description

Onto Innovation is a leader in process control, combining global scale with an expanded portfolio of leading-edge technologies that include: 3D metrology spanning the chip from nanometer-scale transistors to micron-level die-interconnects; macro defect inspection of wafers and packages; metal interconnect composition; factory analytics; and lithography for advanced semiconductor packaging. Our breadth of offerings across the entire semiconductor value chain helps our customers solve their most difficult yield, device performance, quality, and reliability issues. Onto Innovation strives to optimize customers’ critical path of progress by making them smarter, faster and more efficient. Job Summary & Responsibilities About the role 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 technical leadership role. You will work closely with system engineers, algorithm developers, and applications teams to understand complex metrology workflows and data flows. You will set the vision and architecture for AI in metrology, then design, build, and productionize AI systems, including agentic ones, that speed up setup and troubleshooting and improve measurement quality. The emphasis is on agents grounded in real tools and data, with the right human oversight. You will also set technical direction and mentor the AI and ML engineers around you. What you’ll do Set the vision and own the architecture. Define the AI strategy and reference architecture for metrology, choose what to build and what to buy, and keep the roadmap tied to the problems customers actually have. Build it yourself. Write, test, and ship production code — prototypes, agent workflows, evaluation tooling, and the systems that run them — and set the standard by example. Design agent workflows for the metrology lifecycle — guided recipe and model setup, qualification runs, tool matching, and root-cause triage when a measurement drifts. Give agents real tools. Connect them to our simulators, solvers, and tool data so the agent orchestrates and the physics computes. Ground agents in fab knowledge: retrieval manuals, recipes, service tickets, and historical measurement outcomes, with provenance on every answer. Design for trust. Human-in-the-loop checkpoints, confidence and uncertainty carried through every step, full audit trails, and refusal when the evidence is thin — a number without an error bar is not a measurement. Deploy where fabs allow it. On-premises and air-gapped inference, with local or hosted models chosen on latency, cost, and data-control constraints, and a release path that survives customer change control. Build the evaluation harness: golden task sets, tool-call correctness, agent-built versus expert-built recipes compared against the TMU budget, and regression tests on every model or prompt change. Keep the measurement science sound. Surrogate models, parameter inference, tool calibration, and calibrated uncertainty remain the engines agents call. Own the ML behind them where it matters, including domain shift across tools, layers, and fabs. Lead technically. Set standards, run design and code reviews, mentor mid-level and junior engineers, and make the approach and build-versus-buy calls. Raise software quality with unit testing, test automation in CI/CD pipelines, refactoring of legacy code, and static and dynamic analysis. Set the practice for AI-assisted development. Apply AI coding tools responsibly to coding, debugging, 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. Spot gaps in team processes and toolchains, and drive improvements. Translate between fab and model. Turn a customer’s setup or yield problem into a tractable agent workflow, then explain the result to engineers who will judge it against a reference technique. Qualifications What you’ll bring MS or PhD in computer science, electrical engineering, physics, applied mathematics, or equivalent hands-on experience. 6 years of applied ML/AI experience in production, including 3 in technical leadership, leading projects or owning the technical direction of a significant AI system. Experience defining AI strategy and system architecture, and getting a technical vision adopted across engineering. Hands-on depth in LLM-based systems: agents, tool calling, retrieval, and prompt and model evaluation, shipped to real users and monitored after release. Strong ML foundations: deep learning in PyTorch, plus regression, anomaly detection, and calibrated uncertainty — not classification alone. Experience with the MLOps stack: reproducible training, dataset and model versioning, model registries, CI/CD, monitoring, and GPU-aware inference optimization, with agile and DevOps ways of working. A track record of evaluating AI systems rigorously: building test sets, defining metrics, and reporting honestly where a system fails. Deep Python and strong software engineering, including data structures, design patterns, and object-oriented design: you write clean, tested production code every day. 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. Experience developing software on Windows with Visual Studio and Azure DevOps confirm toolchain . A record of owning AI systems in production after launch: monitoring them, diagnosing failures, and shipping updates. Clear technical writing, and the patience to explain an AI system’s decision to a process engineer who will not accept “the model decided” as an answer. What would make us especially interested Strong programming skills in C , C#, or CUDA where the model meets the instrument are a plus. Semiconductor process control experience — metrology, inspection, SPC, yield analytics, or time spent in a fab. Scatterometry or OCD, ellipsometry, reflectometry, interferometry, or e-beam imaging. Tool-calling and MCP frameworks, multi-agent orchestration, vector and hybrid search, LLM fine-tuning, and on-premises LLM serving. Security and IP protection for AI systems, and shipping inside a regulated release process or with customer data that can never leave the site. Physics-informed ML, surrogate modeling, physics-based modeling and numerical simulation, or Bayesian inverse methods such as MCMC or variational inference. Parallel or high-performance computing. Experience in integrating AI into the software development process, such as AI-assisted code review, automated test creation, and requirements analysis. Working here This role is on-site by design: it depends on close, daily collaboration with applications and system engineering. Expect regular time in the office, and occasional time at a fab or customer site. Our customers do not tolerate surprises, neither do we. Work here is reviewed, documented, and qualified before it ships — and once it does, it runs in production lines measuring wafers around the clock. Why Join Onto Innovation? At Onto Innovation, we believe your work should matter—and so should your well-being. That’s why we offer competitive salaries and a comprehensive benefits package designed to support you and your family. From health, dental, and vision coverage to life and disability insurance, PTO, and a 401(k) with employer match, we’ve got you covered. You’ll also enjoy access to our Employee Stock Purchase Program (ESPP), wellness initiatives, and cutting-edge tools—all within a collaborative, inclusive culture where your contributions are valued and recognized. Compensation & Growth • Base Salary Range: $129,600.00 - $194,400.00, offered in good faith and based on experience, location, and qualifications. Additional Rewards: Annual bonus opportunities and potential long-term incentives tied to both company and individual success. Empowering Every Voice to Shape the Future: Onto Innovation is committed to creating a workplace where every qualified candidate has an equal opportunity to succeed. We evaluate applicants based on skills, experience, and potential - without regard to race, color, religion, gender, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by law. We believe diversity of thought and background drives innovation and strengthens our team. Important Note on Export Compliance For certain positions requiring access to technical data, U.S. export licensing review may be necessary for applicants who are not U.S. Citizens, Permanent Residents, or other protected persons under 8 U.S.C. 1324b(a)(3).

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Must-haves

Job Summary & Responsibilities
Build it yourself. Write, test, and ship production code — prototypes, agent workflows, evaluation tooling, and the systems that run them — and set the standard by example.
Design agent workflows for the metrology lifecycle — guided recipe and model setup, qualification runs, tool matching, and root-cause triage when a measurement drifts.
Give agents real tools. Connect them to our simulators, solvers, and tool data so the agent orchestrates and the physics computes.
Ground agents in fab knowledge: retrieval manuals, recipes, service tickets, and historical measurement outcomes, with provenance on every answer.
Design for trust. Human-in-the-loop checkpoints, confidence and uncertainty carried through every step, full audit trails, and refusal when the evidence is thin — a number without an error bar is not a measurement.
Deploy where fabs allow it. On-premises and air-gapped inference, with local or hosted models chosen on latency, cost, and data-control constraints, and a release path that survives customer change control.
Build the evaluation harness: golden task sets, tool-call correctness, agent-built versus expert-built recipes compared against the TMU budget, and regression tests on every model or prompt change.
Translate between fab and model. Turn a customer’s setup or yield problem into a tractable agent workflow, then explain the result to engineers who will judge it against a reference technique.
What you’ll bring MS or PhD in computer science, electrical engineering, physics, applied mathematics, or equivalent hands-on experience.
6 years of applied ML/AI experience in production, including 3 in technical leadership, leading projects or owning the technical direction of a significant AI system.
Experience defining AI strategy and system architecture, and getting a technical vision adopted across engineering.
Hands-on depth in LLM-based systems: agents, tool calling, retrieval, and prompt and model evaluation, shipped to real users and monitored after release.
Strong ML foundations: deep learning in PyTorch, plus regression, anomaly detection, and calibrated uncertainty — not classification alone.
Experience with the MLOps stack: reproducible training, dataset and model versioning, model registries, CI/CD, monitoring, and GPU-aware inference optimization, with agile and DevOps ways of working.
A track record of evaluating AI systems rigorously: building test sets, defining metrics, and reporting honestly where a system fails.
Deep Python and strong software engineering, including data structures, design patterns, and object-oriented design: you write clean, tested production code every day.
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.
Experience developing software on Windows with Visual Studio and Azure DevOps confirm toolchain .
A record of owning AI systems in production after launch: monitoring them, diagnosing failures, and shipping updates.

Nice-to-haves

What would make us especially interested Strong programming skills in C , C#, or CUDA where the model meets the instrument are a plus.
Additional Rewards: Annual bonus opportunities and potential long-term incentives tied to both company and individual success.

Application prep

Use the role’s language and application details while tailoring your materials.

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aimlci/cdllmagilepythonazure

Platform: workday
Effort: long

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401(K)

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