Graph Machine Learning Research Intern

HRL Laboratories•Calabasas, CA

1d•$43 - $48

About The Position

HRL Laboratories pioneers the next frontiers of physical and information science. Delivering transformative technologies in automotive, aerospace and defense, HRL advances the critical missions of its customers to help them remove limitations and create competitive advantage. HRL then transitions the work back to customers – ready for real-world application. For more than 70 years, HRL’s rich portfolio of scientific discoveries and engineering innovations continues to build on each other — often in unexpected, profound and far-reaching ways. As a private company owned jointly by Boeing and GM, HRL prioritizes purpose over profit, significantly advancing the state of the art. HRL Laboratories develops robust intelligent systems that deliver adaptable, autonomous performance improvement solutions for complex missions. Our teams advance human-machine synergy, operationalized machine learning models and complex systems analytics and agents to create scalable, secure technologies. We design novel algorithms and mission-ready solutions that strengthen decision making for autonomous and human-guided systems across national security and commercial applications. Essential Duties: Lead and contribute to cutting-edge research in graph computing and graph machine learning (GML). Design, develop, and evaluate algorithms for graph representation learning, reasoning, and analytics on dynamic, heterogeneous, and large-scale graphs. Apply GML to high-impact domains such as cybersecurity, finance, social science, material science, and intelligent systems. Integrate GML with foundation models (e.g., large language models/LLMs, multimodal models) for tasks like knowledge graph reasoning, graph-augmented retrieval, and trustworthy decision support. Translate research insights into deployable prototypes and production-level software. Author technical publications, invention disclosures, and research presentations for internal and external stakeholders, and support proposal and business development activities.

Requirements

Currently pursuing an M.S. or Ph.D. in Computer Science, Network Science, Artificial Intelligence, Applied Mathematics, or a closely related discipline.
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 and ability to obtain and maintain US Government security clearance.

Nice To Haves

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

Responsibilities

Lead and contribute to cutting-edge research in graph computing and graph machine learning (GML)
Design, develop, and evaluate algorithms for graph representation learning, reasoning, and analytics on dynamic, heterogeneous, and large-scale graphs.
Apply GML to high-impact domains such as cybersecurity, finance, social science, material science, and intelligent systems.
Integrate GML with foundation models (e.g., large language models/LLMs, multimodal models) for tasks like knowledge graph reasoning, graph-augmented retrieval, and trustworthy decision support.
Translate research insights into deployable prototypes and production-level software.
Author technical publications, invention disclosures, and research presentations for internal and external stakeholders, and support proposal and business development activities.