Materials Knowledge Architect

present of AI; Cambridge; Hybrid; GBP 65,000 - 100,000; Full time

Job summary

CuspAI, an AI search engine for new materials, seeks a Materials Knowledge Architect to turn messy scientific data into clean, structured representations and to design schemas and ingestion pipelines for materials data. You will work closely with product and data teams to ensure the data supports AI-powered discovery.

This role sits at the intersection of materials science and AI, building data representations that feed machine learning models for materials discovery and enabling faster, more

Qualifications

• Solid grounding in materials science.
• Experience designing schemas for scientific data.
• Experience designing ingestion pipelines for scientific data.
• Ability to turn messy data into clean, structured representations.

Responsibilities

• Design data representations that feed ML models for materials discovery.
• Collaborate with product and data teams to ensure usable data for AI models.
• Shape how physical property data and chemical designs are captured so models can use them effectively.

Skills

Materials science

Data modeling

Schema design

Data ingestion pipelines

Job description

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.

How it fits the AI era

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.

How it builds AI experience

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.

Worth showing

• 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.

About CuspAI

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.