AI Scientist, Product Lifecycle Management (PLM)

Avathon • Pleasanton, CA

2d • Onsite

About The Position

Avathon is hiring an AI Scientist in Product Engineering to build models for Product Lifecycle Management (PLM) on its Industrial AI platform. The role focuses on using engineering data—including bills of materials (BOMs), part attributes, CAD metadata, drawings, specifications, and engineering change history—to help manufacturers find similar and substitute parts, predict the impact of engineering changes, extract structure from documents, and search engineering information. Models run on Avathon’s Computational Knowledge Graph, which connects parts with suppliers, orders, production, and assets in the field. The scientist will take models from research to production in collaboration with the PLM engineering team.

The position combines engineering-data intelligence, graph and language models, and productization. It includes building and applying machine-learning models, retrieval and LLM-based tools, and Normal Behavior Models, then working with engineering to deploy and monitor them, provide clear explanations, and communicate methods and results to product teams, customers, and leadership. The role also works with AI agents that help engineers and planners act on change-impact insights.

The role is based in the Bay Area, California, and is not remote. Employees are expected to report to Avathon’s Pleasanton office five days a week. The stated annual salary range is $160,000–$240,000; pay may vary based on location and job-related knowledge, skills, and experience.

Requirements

• PhD in Computer Science, Machine Learning, Mechanical Engineering, or a related field, or an MS with at least 5 years of relevant industry experience.
• Strong foundation in machine learning, with depth in at least one of graph learning, NLP/LLMs, or computer vision on documents.
• Experience applying machine learning to engineering or manufacturing data, such as BOMs, CAD, drawings, or part catalogs.
• Strong Python skills and experience with the standard machine-learning stack, including PyTorch, Hugging Face, and graph libraries.
• Experience shipping models to production, not only working in notebooks.

Nice To Haves

• Experience at a PLM or CAD vendor such as Siemens, PTC, Dassault Systemes, Autodesk, or Oracle, or at an engineering-heavy manufacturer.
• Related publications or patents.

Responsibilities

• Build part-similarity, classification, and substitution models using part attributes, BOM structure, and CAD metadata.
• Build models that predict the downstream impact of engineering changes across BOMs, suppliers, and production.
• Extract structured data from drawings, specifications, and engineering documents using vision and language models.
• Apply graph learning to the Computational Knowledge Graph for link prediction, entity resolution, and change-impact analysis.
• Build retrieval and LLM-based tools that let engineers search parts, drawings, and change history in plain language.
• Use Normal Behavior Models to flag unusual change activity, such as spikes in engineering change request volume on a product line.
• Work with engineering to put models into production, including evaluation, monitoring, and clear explanations.
• Work with AI agents that help engineers and planners act on change-impact insights.
• Clearly document methods and results for product teams, customers, and leadership.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree