Senior Applied Scientist - Semantics
Core
Design and deploy structured knowledge systems enabling schema-grounded AI and agent reasoning by transforming unstructured data into structured representations.
Role type
Senior Applied Scientist (Semantics & Knowledge Graphs)
Builds
Scalable semantic infrastructures, GraphRAG pipelines, and hybrid symbolic–neural reasoning systems.
Domain
Artificial Intelligence, Knowledge Graphs, Semantic Architectures
Deliverable
production ML models
Required skills
Knowledge graph design, ontology modeling, structured extraction, entity resolution, graph query optimization, hybrid symbolic-neural system design, Python engineering
Preferred skills
Graph algorithms (PageRank, community detection), GraphRAG implementation, compositional semantic extraction, rule-based inference engines
Technologies
LinkML, OWL, SHACL, RDF, SPARQL, Cypher, Node2Vec, TransE, JSON schema, AST parsing
Responsibilities
Design schema-guided information extraction systems using constrained decoding and function-calling techniques. Develop recursive extraction pipelines for nested entities and cross-document relationships. Build ontology-driven systems and implement knowledge representations using RDF triples or labeled property graphs. Design and optimize entity resolution algorithms using blocking, embedding similarity, and rule-based matching. Develop ontology alignment techniques and graph embedding models. Design hybrid retrieval architectures combining dense vector, sparse retrieval, and graph traversal algorithms. Build validation systems to enforce schema conformance and reduce hallucinations. Integrate structured knowledge systems into GraphRAG pipelines and agent planning frameworks.
Seniority
Senior, hands-on IC
