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About the Role
Join CuspAI's Data team as a Materials Knowledge Architect to design scientific data representations, schemas, and ingestion pipelines, transforming heterogeneous external data into actionable data assets for materials discovery.
Responsibilities
- Design data models representing chemical, structural, and materials property information, mapping diverse external sources to a coherent internal representation.
- Establish and own data quality, validation, deduplication, lineage, and provenance frameworks.
- Maximize the quality, scale, and consistency of data from external sources using expertise in chemistry, physics, and/or materials science.
- Lead the ingestion of data from partners, building robust pipelines to normalize, harmonize, and reconcile data.
- Contribute to identifying, evaluating, and assessing new data sources, partnerships, and data generation opportunities.
- Partner with external data providers to understand their data, agree on formats and standards, and define schema mappings.
- Resolve data quality, provenance, and interoperability issues at the source with external partners.
- Translate modeling needs into ingestion requirements and ML-ready datasets in partnership with research and engineering teams.
- Communicate work and raise awareness of opportunities to improve data models, ingestion processes, and data quality.
Requirements
- Proven experience designing data models and schemas for complex scientific or technical domains.
- Proven experience curating high-quality data assets from complex scientific or technical domains.
- Demonstrable experience ingesting, integrating, and harmonizing data from multiple external sources or partners.
- PhD in Chemistry, Physics, Materials Science, Computational Chemistry, or a related discipline, or equivalent experience in scientific research.
- Expertise in data representation, ontologies, and data modelling.
- Expertise in the curation of high-quality, interoperable data assets.
- Experience working with a broad range of data types used in materials discovery, including experimental and characterization data.
- Experience mapping materials discovery data across heterogeneous sources.
- Deep knowledge of materials and chemical data sources spanning experimental and computational domains.
- Deep knowledge of practical challenges in harmonizing lab-generated data.
- Working knowledge of Python and SQL.
- Experience building data pipelines for experimental and computational data.
- Experience with schema/data-model design (e.g., Pydantic, JSON Schema).
- Familiarity with materials and data science toolkits (e.g., pymatgen, ASE, Pandas/Polars).
- Firsthand experience with how experimental data is produced and pragmatic strategies for modeling it faithfully.
- Strong communicator and proven collaborator.
- Comfortable working directly with external partners.
- Comfortable working with multidisciplinary chemistry/physics/materials and product/engineering teams.
Skills
- Data Architecture
- Schema Design
- Data Modelling
- Data Quality Frameworks
- Data Validation
- Deduplication
- Data Lineage
- Data Provenance
- Data Ingestion
- Data Pipeline Execution
- Data Harmonization
- Data Reconciliation
- Data Source Evaluation
- Partnership Management
- Interoperability
- Python
- SQL
- Pydantic
- JSON Schema
- pymatgen
- ASE
- Pandas
- Polars
- Materials Science Data
- Chemical Data
- Experimental Data
- Characterization Data
- Computed Properties
- Ontologies
Location
- Cambridge
- London
- Amsterdam
- Berlin
Work Type
- Hybrid
Experience Level
- PhD or equivalent experience
- Proven experience designing data models and schemas
- Demonstrable experience ingesting, integrating and harmonizing data
- Expert in data representation, ontologies, data modelling and curation
- Experience working with a broad range of data types
- Deep knowledge of materials and chemical data sources
- Working knowledge of Python and SQL
- Firsthand experience with how experimental data is actually produced
- Experience working in industry (Bonus)
- Familiarity with data engineering concepts (Bonus)
- Experience defining data-sharing agreements (Bonus)
- Experience with high-throughput computational screening workflows (Bonus)
Education Level
- PhD in Chemistry, Physics, Materials Science, Computational Chemistry or a related discipline, or equivalent experience in scientific research.
Salary/Compensations
- Competitive salary
Benefits
- Equity in CuspAI
- 28 days holiday (DE, NL, UK) or 21 days holiday (JP, SG, US), in addition to local public holidays
- 26 weeks (primary caregiver) and 12 weeks (secondary caregiver) of parental leave at full pay
- Professional development budget
About the Company
- CuspAI is a frontier AI company focused on solving the breakthrough materials needed to power human progress.
- Harnessing AI to unlock trillion-dollar materials breakthroughs in months.
- The founding team is world-class in AI, chemistry, and engineering.
- Working on hard and important challenges including energy, clean water, the future of compute, and carbon capture.
- Developing a 'search engine' for next-generation materials.
- An invitation to join a diverse, innovative team at the intersection of AI and materials science.
- Creating impactful partnerships that drive innovation, scalability, and industry collaboration.
- On the cusp of the on-demand materials era.
Equal Opportunity
- 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.