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AI Knowledge Graph Embedding Researcher

Isaac Singh

Full-time · Job · Berlin

About the role

Knowledge graph embeddings are the bridge between symbolic knowledge representation and neural reasoning. A well-trained embedding lets a model traverse relationship paths, infer missing links, and answer questions that require combining multiple facts — capabilities that pure language models still struggle with on structured knowledge tasks. Our knowledge graph covers legal, financial, and regulatory domains across 18 jurisdictions. The embedding model that drives our reasoning capabilities was trained two years ago on a smaller graph. With 3x more entities and 5x more relation types, it's time for a fundamental rethink — not just retraining, but redesigning the embedding architecture to handle the heterogeneous relation types and temporal dynamics our current model ignores. You'll lead the embedding research program: evaluating recent methods from the literature, designing experiments, implementing the most promising approaches, and validating that improved embeddings translate to better downstream task performance.

Responsibilities

  • Evaluate and implement recent knowledge graph embedding architectures
  • Design experiments measuring embedding quality on link prediction and relation inference
  • Adapt embedding approaches to handle temporal and heterogeneous relation types
  • Validate that embedding improvements translate to downstream task quality gains
  • Build the training pipeline for the next-generation embedding model
  • Publish research findings and engage with the knowledge graph ML community

Requirements

  • 5+ years in ML research with knowledge graphs, NLP, or representation learning
  • Deep familiarity with knowledge graph embedding methods: TransE, RotatE, ComplEx
  • Strong PyTorch skills and experience with large-scale embedding training
  • Track record of publications in KG, NLP, or representation learning
  • Ability to translate research findings into production improvements

Benefits

  • Fully remote — EU or US time zones
  • Research conference budget
  • 30 days PTO
  • Health insurance
  • Equity package

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Categories

AI & Machine Learning Engineers NLP & Prompt Engineering

Skills

python pytorch hugging face docker aws

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

AI & Machine Learning Engineers NLP & Prompt Engineering

Skills

python pytorch hugging face docker aws statistics

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