Machine Learning Research Scientist

What you'll do

• Build LLM-powered extraction, memory consolidation, temporal knowledge graph, and causal reasoning systems.
• Research semantic compression, entity resolution, organizational pattern learning, and privacy-preserving cross-organizational learning.
• Publish research findings and contribute to the knowledge graph and organizational intelligence research community.

What they ask for

• 5+ years building novel systems in machine learning, NLP, knowledge graphs, or related areas, evidenced by publications, production implementations, or significant open-source contributions.
• Deep knowledge of knowledge graphs, graph neural networks, or temporal reasoning, demonstrated through shipped systems and architectural exploration.
• Strong ML and NLP foundation, particularly in information extraction, entity resolution, or semantic representation.
• Proficiency in Python and modern ML frameworks, with experience deploying models at scale.

Nice to have

• Graph databases (Neo4j, TigerGraph, Neptune) and query optimization for large-scale graphs.
• Information theory, compression, temporal data structures, causal inference, probabilistic reasoning, or Bayesian methods.

Pay

$150,000 – $300,000 USD (base salary); $120K – $300K USD (compensation; type not specified)

Tools

Python, PyTorch, Neo4j, TigerGraph, Neptune

AI focus

Builds LLM-powered information extraction and organizational memory systems, including temporal knowledge graphs, causal reasoning, pattern prediction, and privacy-preserving learning.