Knowledge Graph Curator

We are seeking a detail-oriented and strategic Knowledge Graph Curator to help grow and curate a Knowledge Graph (KG) of entities and relationships supporting content understanding automation. This role sits at the intersection of AI automation and human judgment, managing incoming requests from partner teams while proactively shaping the growth of the KG to ensure high fidelity, relevance, and connectivity. This person will serve as the expert human-in-the-loop, validating LLM-generated entities and ensuring the graph represents ground truth for the business.

Key Responsibilities

• Act as the primary point of contact for partner teams (Product, Engineering, Analytics) requesting new entities or schema changes
• Triage the backlog of requests by assessing business impact, urgency, and technical feasibility
• Interact with internal tooling to review LLM-generated entities, approving high-confidence data, editing near-misses, and rejecting hallucinations
• Perform rigorous QA on batches of generated entities to ensure adherence to ontological standards and factual accuracy
• Participate in labeling exercises (creation of Golden Sets) to measure model quality and provide training data for classifier/extraction fine-tuning
• Investigate bug reports from downstream users or automated anomaly detection systems
• Manually fix data errors, merge duplicate entities, and resolve conflicting relationships
• Report on request status to partner teams, explain modeling decisions, and educate stakeholders on querying new data
• Onboard new entities to the Knowledge Graph
• Perform data entry and data labeling to support content understanding automation
• Support LLM prompt tuning for content understanding automation

Required Qualifications

• Understanding of Knowledge Graph fundamentals (Nodes, Edges, Properties)
• Experience with taxonomy and ontology, including categorizing data, managing hierarchies, and understanding semantic relationships
• Proficiency navigating complex datasets (experience with SQL, SPARQL, or Cypher is a strong plus)
• Understanding of how Generative AI works, common failure modes (hallucinations), and the importance of ground-truth data in training
• Strong analytical prioritization skills
• Strong attention to detail
• Strong stakeholder communication skills, able to translate complex data modeling concepts for non-technical audiences
• Comfort learning proprietary internal tools, ticketing systems (e.g., Jira), and spreadsheet tools (Excel/Google Sheets)

Ideal Candidate

The ideal candidate is a detail-oriented curator who thrives at the intersection of AI automation and human judgment. They bring strong analytical prioritization skills, a sharp eye for data inconsistencies, and the ability to translate complex technical concepts for non-technical stakeholders. They are comfortable working hands-on with AI-generated data, validating quality, and continuously improving the accuracy and structure of a growing knowledge graph.