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Onto Innovation

Semiconductor process-control metrology and inspection equipment

Lead Artificial Intelligence Engineer - Semiconductor AI Innovation

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeWilmington, MA, USA

About the Job

Requirements

  • At least 5 years of applied machine learning or artificial intelligence experience, including at least 3 years in a technical leadership role.
  • Hands-on expertise in at least two domains: large language models (including retrieval-augmented generation, fine-tuning, agent frameworks, or prompt optimization); predictive modeling (including tool-failure prediction, anomaly detection, or time-series analysis); or computer vision (including defect detection, segmentation, or scanning electron microscope or optical imaging).
  • Strong background in machine-learning systems architecture and production deployment.
  • Advanced Python proficiency; familiarity with C++ or CUDA is a plus.
  • Experience with MLOps stacks, including containers, continuous integration and continuous delivery, Ray Serve or Triton, model registries such as MLflow, and GPU optimization.
  • Strong stakeholder collaboration skills and ability to translate between engineering, operations, and leadership.
  • Demonstrated success delivering AI-powered products into production.

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 current processes, define a future AI and automation architecture, and deliver measurable outcomes.
  • Deploy agentic assistants using large language models, retrieval-augmented generation, and tool integrations via MCP servers to help engineers accelerate tool operation, setup, and maintenance and explain trade-offs using internal documents, logs, and historical inspection outcomes.
  • Lead predictive maintenance work for tool health monitoring and failure detection.
  • Lead computer-vision work for wafer defect detection, segmentation, and classification.
  • Lead development of large-language-model engineering assistants using retrieval-augmented generation and MCP agents to make internal knowledge more accessible.
  • Lead process optimization and yield improvement through data-driven insights and parameter tuning.
  • Lead simulation and digital-twin work to model process behaviors and accelerate research and development.
  • Build retrieval-augmented AI assistants to query internal knowledge bases, tools, and logs.
  • Architect robust pipelines for data ingestion, labeling, storage, and retrieval across large multimodal datasets, including images, telemetry, recipes, and logs.
  • Establish scalable MLOps infrastructure for model registries, monitoring, evaluation, deployment, and governance.
  • Hire, mentor, and manage a team of three engineers focused on LLMs and agents, computer vision and machine learning, and MLOps and data.
  • Work cross-functionally to integrate AI solutions safely and securely into production environments.

Desired Qualifications

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

About the Company

Onto Innovation

Semiconductor process-control metrology and inspection equipment

Onto Innovation provides semiconductor capital equipment and software for process control, metrology, and inspection. Its products include 3D metrology, macro defect inspection, unpatterned wafer quality assessment, and factory analytics software; these tools measure nanoscale transistor features and micron-level die interconnects, while software analyzes the data to give actionable manufacturing insights and customers buy equipment with ongoing service contracts and licenses. The company differentiates itself by offering an end-to-end process-control portfolio that spans front-end fabrication and back-end packaging, a result of the 2019 merger of Nanometrics and Rudolph Technologies, with integrated hardware and analytics that support complex architectures like chiplets. Its goal is to help semiconductor manufacturers increase device yield, boost productivity, and lower total production costs, aligning with capital spending cycles in AI workloads and advanced node technologies.

Company size
1,001-5,000
Company stage
IPO
Headquarters
Wilmington, MA
Founded
2019

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