Lead AI Engineer — Semiconductor AI Innovation

Onto Innovation•Wilmington, MA

2d•Onsite

About The Position

Onto Innovation is seeking a Lead AI Engineer to drive AI-powered solutions for semiconductor equipment operations. This is a hands-on technical leadership role responsible for setting the vision, building a team, and delivering impactful AI solutions at scale. The role involves working closely with senior tool designers, process engineers, and applications teams to understand complex workflows and data flows, and to architect, prototype, and productionize AI solutions that accelerate innovation, improve yields, and reduce tool downtime. The Lead AI Engineer will also grow and mentor a team of three engineers specializing in LLMs, computer vision, predictive modeling, and MLOps.

Requirements

5+ years applied ML/AI experience, with 3+ years in a technical leadership role.
Hands-on expertise with at least two of the following domains: Large Language Models - RAG, fine-tuning, agent frameworks, prompt optimization.
Predictive Modeling - tool failure prediction, anomaly detection, time-series analysis.
Computer Vision - defect detection, segmentation, or SEM/optical imaging.
Strong background in ML systems architecture and production deployment.
Advanced Python proficiency: C++/CUDA familiarity is a plus.
Experience with MLOps stacks: containers, CI/CD, Ray Serve/Triton, model registries (e.g., MLflow), and GPU optimization.
Strong stakeholder collaboration skills and the ability to translate between engineering, operations, and leadership.
Demonstrated success delivering AI-powered products into production.

Nice To Haves

Familiarity with semiconductor manufacturing, inspection, or metrology.
Understanding of fab interfaces and data connectivity ( SECS/GEM, GEM300 ).
Prior experience deploying digital twins or simulation-driven optimization.
Knowledge of vector databases, retrieval pipelines, and hybrid search.
Experience implementing safety, security, and IP protections for AI systems.
Exposure to datasets or tools from KLA, ASML, Applied Materials, Onto, Nova, or similar inspection/metrology vendors.

Responsibilities

Define the AI strategy and architecture for integrating machine learning into core engineering and manufacturing processes.
Partner with tool, process, and applications engineers to map as-is processes and define a to-be AI/automation architecture and deliver measurable outcomes.
Ship agentic assistants for use-cases.
Stand up LLM + RAG + tool integrations (via MCP servers) that help engineers accelerate tool operation/setup/maintenance and explain trade-offs, grounded in internal docs, logs, and historical inspection outcomes.
Lead projects across diverse areas: Predictive maintenance for tool health monitoring and failure detection.
Computer vision for wafer defect detection, segmentation, and classification.
LLM-based engineering assistants using RAG and MCP agents to make internal knowledge more accessible.
Process optimization & yield improvement through data-driven insights and parameter tuning.
Simulation and digital twins to model process behaviors and accelerate R&D.
Build retrieval-augmented AI assistants to query internal knowledge bases, tools, and logs.
Architect robust pipelines for data ingestion, labeling, storage, and retrieval across massive multi-modal datasets (images, telemetry, recipes, logs).
Stand up scalable MLOps infrastructure: model registries, monitoring, evaluation, deployment, and governance.
Hire, mentor, and manage a team of 3 engineers focused on LLM/Agents, CV/ML, and MLOps/Data.
Work cross-functionally to integrate AI solutions into production environments safely and securely.