Materials Data Architect for AI-Driven Discovery

US Health Partners, LLC; Cambridge; Hybrid; GBP 120,000 - 180,000; Full time

Benefits offered by this job

Competitive salary

Equity in the company

Holiday allowance

Parental leave

Professional development budget

Meaningful work

Interdisciplinary teamwork

Job summary

CuspAI in Cambridge is seeking a Materials Knowledge Architect to join the Data team and lead the design of scientific data representations, schemas and ingestion pipelines. You will work with AI researchers and materials scientists to create ML-ready datasets from diverse sources, ensuring quality, provenance and interoperability across experiments, simulations and measurements.

This role offers a collaborative, interdisciplinary environment across offices in Cambridge, London, Amsterdam and

Qualifications

• Proven experience designing data models and schemas for complex scientific domains.
• Experience ingesting, integrating and harmonising data from multiple external sources.
• PhD in Chemistry, Physics, Materials Science or related field, or equivalent experience.
• Expert in data representation, ontologies and interoperable data assets.
• Experience with materials data types and mapping across sources.
• Knowledge of key materials data sources (ICSD, NOMAD, Materials Project).
• Proficient in Python and SQL; building data pipelines.

Responsibilities

• Design data models for chemical, structural and materials property information.
• Own data quality, validation, deduplication, lineage and provenance frameworks.
• Lead data ingestion from partners and build robust pipelines.
• Collaborate with research and engineering teams to translate needs into ingestion requirements.

Skills

Data modeling

Data schemas

Data integration

Python

SQL

Data quality

Communication

Education

PhD in Chemistry/Physics/Materials Science or related field

Tools

Pydantic

JSON Schema

Pymatgen

ASE

Pandas/Polars

SQL tooling

Job description

CuspAI in Cambridge is seeking a Materials Knowledge Architect to join the Data team and lead the design of scientific data representations, schemas and ingestion pipelines. You will work with AI researchers and materials scientists to create ML-ready datasets from diverse sources, ensuring quality, provenance and interoperability across experiments, simulations and measurements.

This role offers a collaborative, interdisciplinary environment across offices in Cambridge, London, Amsterdam and