Senior Applied Scientist, Organizational Memory (PhD Required)

About the role

Applied research role building dynamic context graphs and agentic systems from heterogeneous data, combining graph ML, knowledge representation, information retrieval, and LLMs. PhD and graph research/publications required.

Requirements

• PhD in a technical field with research focus in ML, NLP, knowledge representation, graph learning, or information retrieval
• Research depth in at least two of knowledge graphs, graph ML, information extraction/NLP, or information retrieval
• Peer-reviewed publications at leading venues
• Experience extracting entities, relationships, and events from heterogeneous data
• Experience designing evaluation without clean ground truth
• Strong Python and PyTorch; hands-on graph databases or graph learning frameworks
• Ability to translate research into working systems

What you'll do

• Research dynamic context graphs and graph intelligence
• Build and update context graphs from structured and unstructured data
• Develop entity resolution, relationship and event extraction, and temporal reasoning methods
• Build graph-based methods for LLM reasoning, retrieval, planning, and agents
• Design graph algorithms, embeddings, retrieval, benchmarks, and evaluation
• Prototype research and translate it into scalable production systems

Where this was posted

• LinkedIn

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