Materials Architect

A Materials Knowledge Architect typically needs a solid grounding in materials science plus the ability to turn messy scientific data into clean, structured representations. A strong candidate brings hands on experience designing schemas and ingestion pipelines for scientific data, and can work closely with product and data teams. At a company building an AI search engine for new materials, that means shaping how physical property data and chemical designs are captured so the models can use them well.

AI speeds up the search over candidate materials, but the quality of that search depends on how the underlying scientific data is modeled and cleaned. This role stays human because it requires judgment about what a property means, how experiments are described, and which data is trustworthy. The architect sets the structure that AI then works within.

The role builds AI experience through designing data representations that feed machine learning models for materials discovery. It offers practice in connecting domain science to data pipelines, which is a core skill for AI work in industry and robotics. Building familiarity with schemas, data ingestion and property prediction workflows would help someone step into this kind of work.

• Materials science background
• Scientific data modeling
• Property data handling

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

An AI search engine for new materials that the company says turns a wanted set of physical properties into synthesizable chemical designs in months instead of years of lab trial and error.