Lead AI Engineer

Onto Innovation is seeking a Lead AI Engineer to define the vision and architecture for AI systems that support semiconductor metrology inspection and decision-making. The role combines hands-on engineering with technical leadership to deliver trustworthy, on-prem and air-gapped inference solutions that improve setup, troubleshooting, and measurement quality.

Location

Hillsboro, OR (onsite)

Salary

USD 129,600 - 194,400 per year

Role Responsibilities

• Define AI strategy and reference architecture for metrology, including build versus buy decisions, while keeping the roadmap aligned to customer problems.
• Write, test, and ship production code spanning prototypes, agent workflows, evaluation tooling, and runtime systems; establish engineering standards through hands-on example.
• Design agent workflows across the metrology lifecycle, including guided recipe and model setup, qualification runs, tool matching, and root-cause triage when measurements drift.
• Integrate agent workflows with simulators, solvers, and tool data so agents can orchestrate, while physics computes results.
• Ground agent outputs in fab knowledge using retrieval of manuals, recipes, service tickets, and historical measurement outcomes, with provenance provided for every answer.
• Implement trust mechanisms using human-in-the-loop checkpoints, end-to-end confidence and uncertainty tracking, complete audit trails, and evidence-based refusals when signals are insufficient.
• Deploy solutions in environments where fabs allow it, including on-premises and air-gapped inference, selecting local or hosted models based on latency, cost, and data-control constraints; maintain a release path that supports customer change control.
• Build an evaluation harness with golden task sets, tool-call correctness checks, comparisons between agent-built and expert-built recipes against the TMU budget, and regression testing on model or prompt changes.
• Maintain measurement science integrity by owning the ML components that support surrogate models, parameter inference, tool calibration, and calibrated uncertainty, including handling domain shift across tools, layers, and fabs.
• Set engineering standards through design and code reviews, mentoring mid-level and junior engineers, and making consistent build versus buy calls.
• Improve software quality through unit testing, test automation in CI/CD pipelines, refactoring of legacy code, and static and dynamic analysis.
• Apply AI coding tools responsibly across coding, debugging, documentation, and test generation, and ensure secure, reliable workflows for evaluating AI-generated code using human review, automated testing, static analysis, security scanning, and validation against requirements; identify process and toolchain gaps and drive improvements.
• Translate customer setup or yield issues into tractable agent workflows, then communicate results so engineers can evaluate them against reference techniques.
• Set the AI and ML vision and mentor the AI and ML engineers around you.

Required Qualifications

• 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+ years in technical leadership roles, leading projects, or owning the technical direction of a significant AI system.
• Experience defining AI strategy and system architecture and driving technical vision adoption across engineering.
• Hands-on depth in LLM-based systems, including agents, tool calling, retrieval, and prompt and model evaluation, with systems shipped to real users and monitored after release.
• Strong ML foundations, including deep learning in PyTorch, regression, anomaly detection, and calibrated uncertainty.
• Experience with MLOps, including reproducible training, dataset and model versioning, model registries, CI/CD, monitoring, and GPU-aware inference optimization, with agile and DevOps ways of working.
• Proven experience rigorously evaluating AI systems by building test sets, defining metrics, and reporting failures honestly.
• Deep Python expertise and strong software engineering practices, including data structures, design patterns, and object-oriented design, with a focus on clean tested production code.
• Hands-on experience with AI-assisted development tools, including writing clear prompts, reviewing generated code for errors and incorrect assumptions, and converting it into maintainable production code.
• Working knowledge of software security, intellectual property, data privacy, and responsible AI practices in enterprise environments.
• Strong analytical and troubleshooting skills for complex software systems, including independent research and resolution of difficult technical problems.
• Experience developing software on Windows using Visual Studio and Azure DevOps.
• Experience owning AI systems after launch, including monitoring, diagnosing failures, and shipping updates.
• Clear technical writing skills and the ability to explain AI decisions to process engineers who require more than “the model decided.”

Technologies

• PyTorch, Python
• CI/CD, GPU-aware inference optimization
• Windows, Visual Studio, Azure DevOps
• LLM-based systems, agents, tool calling, retrieval
• Prompt and model evaluation, MLOps, model registries, monitoring
• Unit testing, static and dynamic analysis, security scanning
• Surrogate models, parameter inference, tool calibration, calibrated uncertainty
• TMU budget, regression tests, golden task sets

Benefits

• Health, dental, and vision coverage
• Life and disability insurance
• PTO
• 401(k) with employer match
• Employee Stock Purchase Program (ESPP)
• Wellness initiatives
• Competitive salaries and a comprehensive benefits package

Additional Interest Areas

• Strong programming skills in C++, C#, or CUDA where the model interfaces with instrumentation
• Semiconductor process control experience, including metrology, inspection, SPC, yield analytics, or time 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, including shipping within regulated release processes or with customer data that cannot 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 integrating AI into the software development process, such as AI-assisted code review, automated test creation, and requirements analysis

Working Here

• The role is on-site by design, requiring close, daily collaboration with applications and system engineering.
• Expect regular time in the office, with occasional time at a fab or customer site.
• Work is reviewed, documented, and qualified before release, and once deployed it runs in production lines measuring wafers around the clock.

Compensation & Growth

• Base Salary Range: $129,600.00 - $194,400.00 (offered in good faith based on experience, location, and qualifications)
• Additional Rewards: Annual bonus opportunities and potential long-term incentives tied to both company and individual success

Export Compliance Notice

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).