Senior Applied Scientist - Semantics

About the role

This role sits at the intersection of large language models, knowledge graphs, semantic architectures, and hybrid retrieval systems. The ideal candidate will build systems that transform unstructured data into structured knowledge representations, enforce semantic constraints, and enable hybrid symbolic–neural reasoning in production environments.

What you'll do

• Design schema-guided information extraction systems using zero-shot and few-shot structured prompting
• Develop recursive or multi-stage extraction pipelines capable of handling nested entities, hierarchical structures, and cross-document relationships
• Build ontology-driven systems using frameworks such as LinkML, OWL, SHACL, or similar schema modeling tools
• Design and optimize entity resolution algorithms using techniques such as blocking strategies, embedding similarity, and rule-based matching
• Develop ontology alignment techniques and graph embedding models such as Node2Vec or TransE-style approaches where appropriate

What you need

• 5–9 yrs of experience
• Machine learning
• Python
• LLMs
• Any graduate