Senior Knowledge Graph Architect & Performance Lead

CORTEX; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Cortex is building intelligent knowledge systems for biopharma, aiming to accelerate data interpretation and laboratory automation. We are hiring a senior engineer to lead the development and optimisation of our core knowledge graph, writing production code and guiding architectural decisions with the founders and engineering team.

You will shape graph schemas, entity linking, and query performance, working across science, ML, and backend groups to deliver robust, scalable graph solutions for

Qualifications

• PhD in Computer Science or related field with strong research background is required.
• At least 2 years of industry experience building knowledge graphs or closely related data systems.
• Hands-on experience with graph modeling, data integration, and query development.
• Strong ontology and schema design skills with semantic constraint definitions.
• Experience with one graph platform (Neo4j, Neptune, GraphDB) and query languages (Cypher, SPARQL, or Gremlin).

Responsibilities

• Lead the development and optimisation of core knowledge graph architecture.
• Design schemas, ontologies, and relationship constraints for scientific data.
• Improve entity linking, deduplication, and data quality across sources.
• Write production code for graph components and guide backend integration.

Skills

Knowledge graphs

Ontology design

Graph databases

Graph querying

Performance optimization

Production code

Education

PhD in Computer Science or related field

Tools

Neo4j

Amazon Neptune

GraphDB

Cypher

SPARQL

Gremlin

Job description

Cortex is building intelligent knowledge systems for biopharma, aiming to accelerate data interpretation and laboratory automation. We are hiring a senior engineer to lead the development and optimisation of our core knowledge graph, writing production code and guiding architectural decisions with the founders and engineering team.

You will shape graph schemas, entity linking, and query performance, working across science, ML, and backend groups to deliver robust, scalable graph solutions for