Content Strategist (Knowledge Graph/Taxonomy)

Content Strategist (Knowledge Graph Curator)

Client: Reddit

Location: Ireland – Fully Remote

Contract Duration: 6 months

Role Overview

We are seeking a detail-oriented Knowledge Graph Curator / Content Quality Specialist to support the review, curation and maintenance of large-scale knowledge and content graphs.

The role focuses on ensuring that information within the graph is accurate, relevant, consistently categorised and correctly connected. You will manually review entities, descriptions and relationships, identify inconsistencies, and validate content generated or suggested by AI systems.

The ideal candidate will have hands-on experience working with Knowledge Graphs, structured knowledge systems, content classification, data labelling or related content-quality workflows. Experience with platforms such as Wikipedia, Wikidata or wikiHow can also be relevant where it demonstrates an understanding of how knowledge is structured, categorised and connected.

The role involves human review of AI-generated outputs, so genuine curiosity about Generative AI and Large Language Models (LLMs), along with the ability to learn and apply feedback, is important.

Key Responsibilities

• Knowledge Graph Curation: Manually review, curate and maintain graph content, ensuring entities, descriptions and associated information are accurate and relevant.
• Entity and Relationship Validation: Check whether entities are correctly categorised and connected, and identify incorrect, missing or inconsistent relationships.
• Content Understanding: Assess the meaning and context of content to determine appropriate classifications, relationships and supporting information.
• Data Quality and Consistency: Identify and resolve duplicate entities, inaccurate information, classification errors and inconsistencies across large-scale graphs.
• AI/LLM Output Review: Evaluate AI-generated or AI-suggested entities and content, approving accurate results, correcting near matches and rejecting misleading or incorrect outputs.
• Data Labelling and Classification: Apply established taxonomies, annotation guidelines and classification rules to ensure consistent, high-quality data.
• Quality Assurance: Perform structured quality checks, investigate recurring errors and support continuous improvements to graph accuracy and reliability.
• Feedback and Collaboration: Work with relevant teams to address quality issues, incorporate feedback and improve curation guidelines and review processes.

Essential Skills and Experience

• Hands-on experience reviewing, curating or maintaining Knowledge Graphs, content graphs or comparable structured knowledge systems.
• Understanding of how entities, attributes, categories and relationships are organised and connected.
• Experience manually reviewing content or structured data for accuracy, relevance, consistency and completeness.
• Experience in at least one of the following areas: data annotation, data labelling, taxonomy, content classification, data curation or quality assurance.
• Strong attention to detail and the ability to identify duplicates, inaccuracies, ambiguous information and inconsistent relationships.
• Good analytical and critical-thinking skills, with the ability to make sound content-quality judgements using established guidelines.
• Genuine interest in Generative AI and LLMs, including curiosity about how models generate information and how their outputs can be evaluated.
• Ability to receive feedback constructively, adapt decisions and apply guidance consistently.

Desirable Experience

• Experience working with large-scale Knowledge Graphs or content graphs.
• Familiarity with structured knowledge platforms or repositories such as Wikipedia, Wikidata or wikiHow.
• Experience reviewing AI-generated information, identifying hallucinations or validating model-suggested entities and relationships.
• Experience maintaining taxonomies, metadata, ontologies or classification schemas.
• Background in linguistics, computational linguistics, language data, NLP, knowledge management or semantic content.
• Experience developing or following annotation guidelines, quality-control processes or reference datasets.

Ideal Candidate Profile

The ideal candidate combines strong content understanding and attention to detail with practical experience in structured knowledge, graph curation or related quality-review workflows.

You do not need to be an AI engineer or have deep technical knowledge of LLMs. However, you should be genuinely interested in AI-generated content and comfortable evaluating its accuracy, consistency and relevance.

Candidates from linguistics, knowledge management, taxonomy, data annotation, content classification and structured-data backgrounds may be suitable, provided they can demonstrate relevant hands-on review experience.

What Success Looks Like

• Accurate and consistent graph entities, descriptions and relationships.
• Effective identification and correction of duplicate, misleading or incorrectly connected information.
• Consistent application of classification and quality guidelines.
• Reliable human review of AI-generated or AI-suggested content.
• A constructive approach to feedback, learning and continuous quality improvement.