Materials Knowledge Architect

The Role

Due to rapid company growth and expanding external data partnerships, we are seeking a Materials Knowledge Architect to join our Data team and lead the design of our scientific data representations, schemas, and ingestion pipelines.

Your impact

This is an exciting opportunity to join the data team at CuspAI, working closely with world-leading AI experts and materials science researchers to design the data models and ingestion pipelines that turn heterogeneous external data into actionable data assets. Much of the data that powers our models arrives in different formats, schemas and standards. As a Materials Knowledge Architect you will own how that data is represented, modelled and brought into CuspAI, and be foundational in ensuring its quality at scale so it can fuel the discovery of the next generation of materials.

Partner & Interdisciplinary Collaboration

• Partner directly with external data providers to understand their data, agree formats and standards, define schema mappings, and resolve quality, provenance and interoperability issues at the source.

• Work in partnership across research and engineering teams to translate modelling needs into ingestion requirements and ML-ready datasets.

• Communicate your work and raise awareness of opportunities to improve data models, ingestion processes and overall data quality.

Must Have Skills and Qualifications

• Proven experience designing data models and schemas for complex scientific or technical domains, and curating high-quality data assets from them.

• Demonstrable experience ingesting, integrating and harmonising data from multiple external sources or partners, each with differing formats, schemas and quality levels.

• PhD in Chemistry, Physics, Materials Science, Computational Chemistry or a related discipline, or equivalent experience in scientific research.

• Expert in data representation, ontologies, data modelling and the curation of high-quality, interoperable data assets.

• Experience working with a broad range of data types used in materials discovery — especially experimental and characterization data (e.g. synthesis parameters and processing conditions, XRD patterns, spectroscopy, adsorption isotherms, device measurements) alongside computed properties (e.g. DFT-derived formation energies, band gaps) — and mapping them across heterogeneous sources.

• Deep knowledge of materials and chemical data sources spanning both experimental and computational domains (e.g. ICSD, the Cambridge Structural Database, NOMAD, Materials Project), and of the practical challenges of harmonizing lab-generated data — instrument outputs, ELN/LIMS records, unit conventions, provenance — into interoperable, ML-ready assets.

• Working knowledge of Python and SQL with experience building data pipelines for experimental and computational data, including schema/data-model design (e.g. Pydantic, JSON Schema) and familiarity with materials and data science toolkits (e.g. pymatgen, ASE, Pandas/Polars).

• Firsthand experience with how experimental data is actually produced — lab workflows, instrument quirks, incomplete metadata — and pragmatic strategies for modeling it faithfully rather than forcing it into idealized schemas.

• Strong communicator and a proven collaborator, comfortable working directly with external partners as well as multidisciplinary chemistry/physics/materials and product/engineering teams.
  
  

Bonus Points (But Not Critical)

• Experience working in industry at a materials, chemicals, energy or deep-tech company, or closely with industry at a research institution or national lab.

• Familiarity with data engineering concepts — ETL/ELT pipelines, schema validation, data contracts, workflow orchestration (e.g. Airflow, Dagster) — and experience running jobs on cloud-based infrastructure.

• Experience defining data-sharing agreements, data standards or interchange formats with external partners.

• Experience with high-throughput computational screening workflows and DFT codes (e.g. VASP, Quantum ESPRESSO).

What We Offer

• A competitive salary: We value and reward impact and growth

• Equity in CuspAI: You have a stake in the success of the company

• Time off to stay fresh: 28 days holiday (DE, NL, UK) or 21 days holiday (JP, SG, US), in addition to local public holidays

• ‘Gold Standard’ parental leave: 26 weeks (primary caregiver) and 12 weeks (secondary caregiver) at full pay - we look after you and your family while we work on the most important materials discovery problems together

• Professional development budget: We invest in your career development so you can stay up to date with the latest industry knowledge or add to your skills to increase impact and growth

• Solve meaningful problems: See how your work has a direct impact on advancing materials science and solving sustainability and climate-related problems through the creation and application of bleeding-edge SOTA technology and revolutionary techniques

• True interdisciplinary teamwork: Be part of a deeply collaborative environment bridging AI research, computational chemistry, and experimental science - work with world-class researchers and engineers who enjoy sharing knowledge and supporting each other

Join us in shaping the future of materials with AI. Together, we can create groundbreaking solutions for a more sustainable world.


CuspAI is an equal opportunities employer committed to building a diverse and inclusive workplace. We do not discriminate on the basis of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding), veteran status, or any other basis protected by applicable law.

We actively encourage applications from all backgrounds and value the unique perspectives and contributions that diversity brings to our team.

Please let us know if you require any specific adjustments during or after the interview process. We will do everything we can within reason to accommodate.