About this role
Onto Innovation seeks a founding Lead AI Engineer for its AI & Advanced Analytics team in Wilmington, MA (on-site). This hands-on technical leader will set the AI strategy, build a team of three AI/ML engineers, and deliver production systems that improve semiconductor equipment operations, manufacturing yield, and tool uptime. The role reports to the Senior Director, Engineering.
Responsibilities
- Work with tool designers, process engineers, and applications teams to map workflows and data flows, design AI and automation architecture, and deliver measurable outcomes.
- Architect, prototype, and deploy solutions for predictive tool maintenance, wafer defect detection and classification, process and yield optimization, and simulation or digital twins.
- Build LLM-based engineering assistants using RAG, MCP-connected tools, and internal documents, logs, and inspection outcomes to support tool setup, operation, and maintenance.
- Design ingestion, labeling, storage, and retrieval pipelines for images, telemetry, recipes, and logs. Establish MLOps infrastructure for model registries, monitoring, evaluation, deployment, and governance; integrate systems into production safely and securely.
- Hire, mentor, and manage engineers focused on LLMs and agents, computer vision and ML, and MLOps and data. Within 90 days, produce an opportunity map and prioritized roadmap; within six months, deliver a production pilot and hire two engineers; within 12 months, integrate multiple systems with measurable impact.
Required qualifications
At least 5 years of applied ML/AI experience, including 3 or more years in technical leadership; hands-on expertise in at least two of LLMs, predictive modeling, and computer vision; ML systems architecture and production deployment experience; advanced Python; experience with containers, CI/CD, serving, model registries, and GPU optimization; strong stakeholder collaboration; and a record of delivering AI products to production. C++ or CUDA familiarity is a plus.
Preferred
Semiconductor inspection or metrology knowledge; SECS/GEM or GEM300; digital twins; vector databases and hybrid search; AI safety, security, and IP protections; or exposure to major inspection and metrology vendors. The listed stack includes OpenAI, Anthropic, Hugging Face, LangChain, LlamaIndex, PyTorch, TensorFlow, scikit-learn, XGBoost, OpenCV, Kornia, Triton, Ray Serve, MLflow, Kubernetes, and Kafka. Benefits include health, dental, vision, life and disability coverage, PTO, a 401(k) with employer match, and an employee stock purchase program. Some applicants may require export licensing review for access to technical data.