Materials Knowledge Architect

About the company

CuspAI is a developer of AI-powered materials discovery platforms that use synthesis-aware generative models to design optimized molecular compositions.

About this role

ABOUT CUSPAI 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. 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. WHAT YOU WILL DO 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. 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