CuspAI is the frontier AI company on a mission to solve the breakthrough materials needed to power human progress. While nature took billions of years to perfect molecules, we are harnessing AI to unlock trillion-dollar materials breakthroughs in months, not millennia. Our founding team is the most cited in the world, comprised of world-class researchers in AI, chemistry and engineering.
We are working on some of the hardest and most important challenges including energy, clean water, the future of compute, and carbon capture, and this is just the start of what our 'search engine' for next-generation materials will unlock.
We invite you to be part of a diverse, innovative team at the intersection of AI and materials science, working to create impactful partnerships that drive innovation, scalability, and industry collaboration. This work matters. Your work matters.
We’re on the cusp of the on-demand materials era. Join us.
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.
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.
Data Architecture & Schema Design
Design the data models that represent chemical, structural and materials property information, and define how diverse external sources map onto a coherent, interoperable internal representation.
Establish and own data quality, validation, deduplication, lineage and provenance frameworks so that every ingested dataset is trustworthy, traceable and ML-ready.
Use your expertise in chemistry, physics and/or materials science to maximise the quality, scale and consistency of data flowing in from external sources.
Ingestion & Data Pipeline Execution
Lead the ingestion of data from partners — industrial collaborators, instrument and simulation vendors, research institutions and commercial data providers — building robust pipelines that normalise, harmonise and reconcile data arriving in varied formats and standards.
Contribute to the data team's efforts to identify, evaluate and assess new data sources, partnerships and data generation opportunities, including computational (e.g. DFT, molecular dynamics) and experimental campaigns.
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.
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.
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).
This role could be based in our Cambridge, London, Amsterdam or Berlin offices, with the expectation of being in the office three days per week. Additionally, there may be regular travel required to other locations for collaboration and project work.
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.