About Grafton Sciences
We’re building AI systems with general physical ability — the capacity to experiment, engineer, or manufacture anything. We believe achieving this is a key step towards building superintelligence. With deep technical roots and real-world progress at scale (e.g., a $42M NIH project), we’re pushing the frontier of physical AI. Joining us means inventing from first principles, owning real systems end-to-end, and helping build a capability the world has never had before.
About the Role
We’re seeking a Senior Ontology & Knowledge Graph Engineer to design, implement, and evolve the semantic structures that organize knowledge across autonomous systems and complex workflows. You’ll build ontologies, schemas, and graph-based representations that enable consistent interpretation, interoperability, and reasoning across agents, tools, and data sources.
This role focuses on knowledge modeling at system scale: defining meaning, constraints, and relationships that remain stable under continuous updates while supporting downstream reasoning, planning, and learning systems.
Responsibilities
Design and maintain ontologies and knowledge graph schemas that represent entities, relations, events, and processes across complex domains.
Implement graph-based knowledge systems (property graphs, RDF/OWL, or hybrid approaches) that support querying, inference, and evolution over time.
Define semantic constraints, typing systems, and validation rules to ensure consistency, correctness, and interpretability of knowledge.
Build ingestion and update mechanisms that integrate heterogeneous data sources into unified semantic representations.
Collaborate with agents, ML, and systems teams to ensure knowledge representations are usable by planners, reasoning engines, and learning systems.
Develop tooling for ontology evolution, versioning, alignment, and impact analysis as schemas and domains change.
Support reasoning and decision pipelines by enabling symbolic queries, rule-based inference, or hybrid neuro-symbolic integration.
Act as a cross-functional technical partner, translating domain knowledge into formal structures and ensuring semantic layers scale with system complexity.
Qualifications
Strong background in ontology engineering, knowledge representation, semantic modeling, or knowledge graphs.
Experience designing and implementing ontologies or schemas for complex domains (e.g., processes, workflows, scientific data, enterprise systems).
Familiarity with graph data models and technologies (e.g., RDF/OWL, SPARQL, property graphs, Neo4j, TigerGraph, or similar).
Understanding of computational logic, typing systems, constraints, or rule-based inference.
Comfort working alongside ML and data systems, and bridging symbolic semantics with statistical or learned components.
Experience designing abstractions that support large-scale, evolving, real-time knowledge updates.
High-agency engineer who enjoys defining structure in ambiguous domains and building semantic systems from first principles.
MS degree (or equivalent) required. PhD preferred.
Above all, we look for candidates who can demonstrate world-class excellence.
Compensation
We offer competitive salary, meaningful equity, and benefits.
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What We Do
Grafton Sciences is pioneering AI systems with general physical ability, the capacity to experiment, engineer, or manufacture anything. By pairing this physical substrate with advanced learning architectures, our goal is to build superintelligence. Backed by leading partners, including ARPA-H, we are redefining how the physical world is queried at scale.
