Materials Knowledge Architect
Core
Design scientific data representations, schemas, and ingestion pipelines to transform heterogeneous external materials data into actionable, ML-ready assets for AI-driven materials discovery.
Role type
Senior IC data architect (materials science domain)
Builds
Interoperable data models and ingestion pipelines for chemical, structural, and materials property information
Domain
AI for materials discovery + materials science data engineering
Deliverable
production ML models
Required skills
Data modeling and schema design, data ingestion and harmonization, ontology design, data curation, domain expertise in chemistry/physics/materials science, Python, SQL, materials data toolkits (pymatgen, ASE, Pandas/Polars)
Preferred skills
ETL/ELT pipeline experience, workflow orchestration (Airflow, Dagster), cloud infrastructure, high-throughput computational screening workflows, DFT code experience (VASP, Quantum ESPRESSO)
Responsibilities
Design data models for chemical/structural/materials property information; establish data quality, validation, deduplication, lineage, and provenance frameworks; lead ingestion of data from partners (industrial collaborators, vendors, research institutions); partner with external providers to define schema mappings and resolve interoperability issues; collaborate with research and engineering teams to translate modeling needs into ingestion requirements
Seniority
Senior, hands-on IC
