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Lead AI Engineer
Onto Innovation. Define the AI strategy and reference architecture for semiconductor metrology .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in defining AI strategy and architecture for semiconductor metrology, with a strong foundation in machine learning and software engineering. Capable of leading technical projects, mentoring engineers, and ensuring high-quality AI systems in production environments.
Highest-signal resume keywords
AI Strategy DefinitionMachine Learning FoundationsTechnical LeadershipSoftware Engineering in PythonMLOps Experience
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningDeep Learning in PyTorchAI-Assisted Development ToolsC++ ProgrammingC# ProgrammingCUDA ProgrammingData StructuresDesign PatternsObject-Oriented DesignPhysics-Informed ML
Soft Skills
Analytical SkillsTroubleshooting SkillsTechnical WritingProblem-Solving Skills
Tools & Technologies
Visual StudioAzure DevOpsCI/CDGPU-Aware Inference OptimizationTest Automation
Industry Keywords
Semiconductor MetrologyProduction AI SystemsTool-Calling FrameworksMulti-Agent OrchestrationRegulated Release Processes
Tech Stack
Tools & technologiesAzurePythonPyTorchC++
About the role
Key responsibilities & impact- Define the AI strategy and reference architecture for semiconductor metrology
- Choose what to build and what to buy and maintain a customer-focused roadmap
- Write, test, and ship production code, prototypes, agent workflows, evaluation tooling, and supporting systems
- Design agent workflows for recipe/model setup, qualification runs, tool matching, and root-cause triage
- Connect agents to simulators, solvers, and instrument data
- Ground agents in manuals, recipes, service tickets, and historical measurement outcomes with provenance
- Implement human-in-the-loop checkpoints, confidence and uncertainty tracking, audit trails, and evidence-based refusal
- Deploy inference on-premises and in air-gapped environments under customer change-control constraints
- Build evaluation harnesses with golden task sets, tool-call correctness tests, recipe comparisons, and regression tests
- Own ML for surrogate models, parameter inference, tool calibration, calibrated uncertainty, and domain shift
- Set technical standards, run design and code reviews, mentor engineers, and make build-versus-buy decisions
- Improve software quality through unit testing, CI/CD test automation, refactoring, and static/dynamic analysis
- Establish responsible AI-assisted development workflows for coding, debugging, documentation, and test generation
- Translate fab and yield problems into agent workflows and explain results to process engineers
- Collaborate daily with applications and system engineering teams; occasionally work at fabs or customer sites
- Monitor, document, qualify, and maintain AI systems running in production semiconductor manufacturing lines
Requirements
What you’ll need- 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
- 3+ years in technical leadership, leading projects or owning the technical direction of a significant AI system
- Experience defining AI strategy and system architecture and getting technical vision adopted across engineering
- Hands-on experience with LLM-based systems, including agents, tool calling, retrieval, and prompt/model evaluation, 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: reproducible training, dataset/model versioning, model registries, CI/CD, monitoring, and GPU-aware inference optimization
- Experience rigorously evaluating AI systems through test sets, metrics, and failure reporting
- Deep Python and strong software engineering, including data structures, design patterns, and object-oriented design
- Hands-on experience with AI-assisted development tools
- Understanding of software security, intellectual property, data privacy, and responsible AI
- Strong analytical and troubleshooting skills and ability to solve complex technical problems independently
- Experience developing software on Windows with Visual Studio and Azure DevOps
- Record of owning AI systems in production after launch
- Clear technical writing and ability to explain AI system decisions to process engineers
- C++, C#, or CUDA programming skills are a plus
- Semiconductor process control experience is a plus
- Experience with scatterometry, OCD, ellipsometry, reflectometry, interferometry, or e-beam imaging is a plus
- Experience with tool-calling/MCP frameworks, multi-agent orchestration, vector/hybrid search, LLM fine-tuning, or on-premises LLM serving is a plus
- Experience with security/IP protection for AI systems and regulated release processes is a plus
- Experience with physics-informed ML, surrogate modeling, physics-based modeling, numerical simulation, or Bayesian inverse methods is a plus
- Parallel or high-performance computing experience is a plus
- Experience integrating AI into software development processes is a plus
Benefits
Comp & perks- Health, dental, and vision coverage
- Life and disability insurance
- Paid time off (PTO)
- 401(k) with employer match
- Employee Stock Purchase Program (ESPP)
- Annual bonus opportunities
- Potential long-term incentives tied to company and individual success
- Wellness initiatives
- Cutting-edge tools
- Collaborative, inclusive culture