Lead AI Engineer

Job Title

Lead AI Engineer

Role Summary

Hands-on technical leadership role to define and deliver AI systems for semiconductor metrology. Work closely with systems, algorithm, and applications teams to set AI strategy and architecture, build production software and agentic workflows, and mentor engineers.

The role focuses on agent-driven workflows grounded in tool simulators, measurements, and process knowledge, with strong emphasis on trust, auditability, and deployability in on-premises or air-gapped customer environments.

Experience Level

Senior - typically 6+ years of applied ML/AI experience in production, including 3+ years of technical leadership or owning the technical direction of significant AI systems.

Responsibilities

Lead design, development, and production operation of AI and agent systems for metrology; set technical direction and standards.

• Define AI strategy and reference architecture for metrology; prioritize build vs buy and maintain a roadmap tied to customer problems.
• Design, implement, test, and ship production code, prototypes, agent workflows, and evaluation tooling.
• Create agent workflows for recipe setup, qualification, tool matching, and root-cause triage.
• Integrate agents with simulators, solvers, and tool data so agents orchestrate while physics-based components compute.
• Ensure agents are grounded in manuals, recipes, service tickets, and measurement history with provenance and audit trails.
• Design human-in-the-loop controls, uncertainty reporting, and refusal behaviors where evidence is insufficient.
• Deliver deployable solutions for on-premises and air-gapped environments, optimizing for latency, cost, and data-control constraints.
• Build evaluation harnesses: golden task sets, tool-call correctness, regression tests, and metrics for system quality.
• Lead code and design reviews, mentor engineers, improve software quality, and raise CI/CD and testing standards.
• Apply AI-assisted development responsibly: review generated code, enforce testing and security checks, and integrate into team toolchains.

Requirements

Must-have:

• 6+ years of applied ML/AI experience in production, including 3+ years in technical leadership or owning major AI systems.
• Proven experience defining AI strategy and system architecture and driving adoption across engineering teams.
• Hands-on experience with LLM-based systems: agents, tool calling, retrieval, and model/prompt evaluation in production.
• Strong ML foundations: deep learning with PyTorch, regression, anomaly detection, and calibrated uncertainty methods.
• Experience with MLOps: reproducible training, dataset/model versioning, model registries, CI/CD, monitoring, and GPU-aware inference optimization.
• Track record of rigorous evaluation: building test sets, defining metrics, and monitoring/diagnosing deployed systems.
• Deep Python and strong software engineering skills: clean, tested production code, data structures, and design patterns.
• Practical experience using AI-assisted development tools and critically reviewing generated code.
• Working understanding of software security, IP, data privacy, and responsible AI in enterprise deployments.
• Experience developing software on Windows with Visual Studio and Azure DevOps toolchain.
• Experience owning AI systems in production post-launch: monitoring, diagnosing failures, and shipping updates.
• Clear technical writing and the ability to explain AI system decisions to engineering and process teams.

Nice-to-have:

• Strong programming in C++, C#, or CUDA for low-level integration with instruments.
• Semiconductor process control experience (metrology, inspection, SPC, yield analytics) or time in a fab.
• Domain knowledge: scatterometry/OCD, ellipsometry, reflectometry, interferometry, or e-beam imaging.
• Experience with tool-calling/MCP frameworks, multi-agent orchestration, vector/hybrid search, and LLM fine-tuning or on-prem LLM serving.
• Security/IP protection experience for AI systems and operating within regulated release processes or air-gapped environments.
• Physics-informed ML, surrogate modeling, Bayesian inverse methods, or numerical simulation experience.
• Parallel or high-performance computing experience.

Education Requirements

MS or PhD in Computer Science, Electrical Engineering, Physics, Applied Mathematics, or equivalent hands-on experience is expected; the posting explicitly allows equivalent practical experience in lieu of an advanced degree.

About the Company

Company: Onto Innovation

Headquarters: Wilmington, MA, United States

Onto Innovation develops process-control solutions for the semiconductor industry, offering optical inspection, 3D metrology, defect inspection, composition analysis, factory analytics, and lithography technologies to improve yield, performance, and reliability across the semiconductor value chain.