AI Knowledge Graph Embedding Researcher

Altrix Systems

Remote · $160k – $200k / year · Full-time · Posted 4 months ago

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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.

Fully remote.

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