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AI Temporal Knowledge Graph Update Developer

Isaac Singh

Full-time · Job · New_York

About the role

Knowledge graphs encode what is true. Temporal knowledge graphs encode what was true and when — capturing that an entity's properties change over time (a company's CEO changes, a country's capital changes, a drug's regulatory status changes) and that relationships between entities have validity periods. Without temporal modeling, a knowledge graph becomes increasingly stale: the 'CEO of' edge points to the previous CEO because the graph was never updated when the transition occurred. We build knowledge intelligence systems for 18 enterprise clients. Temporal knowledge graph maintenance is the data freshness challenge: keeping the knowledge graph current as new information arrives through news feeds, regulatory filings, corporate announcements, and public data updates. The problem has two components: detecting when an existing fact has changed (change detection), and extracting the new fact with its valid-from timestamp (temporal fact extraction). You'll build the temporal knowledge graph update pipeline: change detection models that identify when new text contradicts existing graph facts, temporal fact extraction that identifies the validity period of new assertions, and the update reconciliation system that handles conflicting information from multiple sources.

Responsibilities

  • Build change detection models identifying when new text assertions contradict or supersede existing knowledge graph facts
  • Implement temporal fact extraction capturing valid-from and valid-to timestamps from news and document sources
  • Develop update reconciliation handling conflicting assertions from multiple authoritative sources
  • Create knowledge graph provenance tracking linking each fact to its source and update history
  • Build temporal query support for knowledge graph queries with time-bounded fact retrieval
  • Monitor graph freshness measuring lag between real-world fact changes and corresponding graph updates

Requirements

  • 3+ years in NLP engineering or knowledge graph development with temporal reasoning or information extraction experience
  • Strong Python and transformer model skills for fact extraction and change detection
  • Understanding of knowledge graph data models, temporal representation, and ontology update semantics
  • Experience handling conflicting information from multiple sources with provenance tracking
  • Familiarity with RDF/SPARQL, property graphs, or similar knowledge representation frameworks

Benefits

  • Fully remote with flexible schedule
  • Health and dental insurance
  • $3,000 annual professional development budget
  • 401(k) with match
  • Equity options

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Categories

AI & Machine Learning Engineers NLP & Prompt Engineering

Skills

python pytorch hugging face sql fastapi

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

AI & Machine Learning Engineers NLP & Prompt Engineering

Skills

python pytorch hugging face sql fastapi docker

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