Content Labeler

Content Labeler Remote (UK) 6 Months Contract Description: We are looking for a detail-oriented and strategic Knowledge Graph Curator. In this role, you will sit at the intersection of AI automation and human judgment. You will not only manage incoming requests from partner teams but also proactively shape the growth of our Knowledge Graph (KG) to ensure high fidelity, relevance, and connectivity. You will serve as the expert human-in-the-loop, validating LLM-generated entities and ensuring our graph represents the "ground truth" for the business. What You'll Do 1. Pipeline Management & Prioritization ● Manage Inbound Requests: Act as the primary point of contact for partner teams (Product, Engineering, Analytics) requesting new entities or schema changes. ● Strategic Prioritization: Triage the backlog of requests by assessing business impact, urgency, and technical feasibility. 2. AI-Assisted Curation & Human-in-the-Loop ● Oversee Automation: Interact with internal tooling to review entities generated by Large Language Models (LLMs). You will approve high-confidence data, edit near-misses, and reject hallucinations. ● Quality Validation: Perform rigorous QA on batches of generated entities to ensure they adhere to the strict ontological standards and factual accuracy required by the KG. ● Model Feedback Loops: Participate in ad-hoc labeling exercises (creation of Golden Sets) to measure current model quality and provide training data to fine-tune classifiers and extraction algorithms. 3. Data Integrity & Stakeholder Management ● Manual Curation & Debugging: Investigate bug reports from downstream users or automated anomaly detection systems. You will manually fix data errors, merge duplicate entities, and resolve conflicting relationships. ● Feedback & Reporting: Close the loop with partner teams. You will report on the status of their requests, explain why certain modeling decisions were made, and educate stakeholders on how to best query the new data. Required Skills Technical & Domain Expertise: ● Knowledge Graph Fundamentals: Understanding of graph concepts (Nodes, Edges, Properties) ● Taxonomy & Ontology: Experience categorizing data, managing hierarchies, and understanding semantic relationships between entities. ● Data Literacy: Proficiency in navigating complex datasets. Experience with SQL, SPARQL, or Cypher is a strong plus. ● AI/LLM Familiarity: Understanding of how Generative AI works, common failure modes (hallucinations), and the importance of ground-truth data in training. Operational & Soft Skills ● Analytical Prioritization: Ability to look at a list of 50 tasks and determine the 5 that will drive the most business value. ● Attention to Detail: An "eagle eye" for spotting inconsistencies, typos, and logical fallacies in data. ● Stakeholder Communication: Ability to translate complex data modeling concepts into clear language for non-technical product managers and business stakeholders. ● Tool Proficiency: Comfort learning proprietary internal tools, ticketing systems (e.g., Jira), and spreadsheet manipulation (Excel/Google Sheets).