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Graph Machine Learning Research Intern

LinkedIn Physics World Calabasas, CA
Not Applicable Posted April 5, 2026 Job link
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Requirements
  • Hands-on experience with graph mining, graph matching, geometric deep learning, and applied GML problems.
  • Proficiency in Python (preferred) or another major programing language (e.g., C++, Java) and deep learning libraries and frameworks (e.g., PyTorch Geometric).
  • Experience with knowledge graphs, ontologies, graph schemas (e.g., RDF, LPG), graph databases (e.g., Neo4J, TigerGraph), and query languages (e.g., Cypher, SPARQL).
  • Experience with large-scale data processing and distributed systems (e.g., Ray, Spark), and optionally with real-time streaming pipelines or online learning pipelines.
  • Experience with bridging GML with NLP, computer vision, multi-modal AI, and agent-based systems.
  • Track record of peer-reviewed publications in premier AI/ML venues (e.g., NeurIPS, ICLR, KDD, WWW, AAAI, ICML, SIGMOD).
  • US Citizenship with the ability to obtain and maintain a US Government Security Clearance
  • Must be enrolled in an educational program following the end of your intern assignment
Preferred Skills
  • Proficiency in Python (preferred) or another major programing language (e.g., C++, Java) and deep learning libraries and frameworks (e.g., PyTorch Geometric).
  • Deep expertise in one or more of the following areas:
  • Graph neural networks (GNNs), graph transformers, and geometric deep learning
  • Temporal/dynamic graph learning and event forecasting
  • Subgraph matching and pattern discovery in large-scale graphs
  • Distributed graph computing (GPU/TPU clusters, distributed graph engines)
  • Heterogeneous, multi-relational, and knowledge graphs
  • Resource-efficient, edge, or federated graph learning
  • Graph-based reasoning, multi-hop inference, and neuro-symbolic AI
  • Graph foundation models and multimodal graph learning
  • Graph-augmented LLMs and agent-based reasoning on graphs
  • Graph-based program analysis and optimization
  • Trustworthy AI, model interpretability, and explainability
Education
  • (Not required) – Currently pursuing an M.S. or Ph.D. in Computer Science, Network Science, Artificial Intelligence, Applied Mathematics, or a closely related discipline.
  • (Not required) – Must be enrolled in an educational program following the end of your intern assignment