Graph Machine Learning Research Intern
HRLCalabasas, California
Internship
Posted March 4, 2026
$43 - $48/hr
Not Specified
Visa Sponsored
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Job Description
This internship involves conducting cutting-edge research in graph machine learning, developing algorithms for graph analytics, and applying GML techniques to various high-impact domains, with opportunities to translate research into deployable software and contribute to scientific publications.
Key Responsibilities
- Lead and contribute to research in graph computing and graph machine learning (GML).
- Design, develop, and evaluate algorithms for graph representation learning, reasoning, and analytics on large-scale, dynamic, and heterogeneous graphs.
- Apply GML techniques to domains such as cybersecurity, finance, social science, material science, and intelligent systems.
- Integrate GML with foundation models like large language models (LLMs) and multimodal models for knowledge graph reasoning and decision support.
- Translate research insights into deployable prototypes and production-level software.
- Author technical publications, invention disclosures, and research presentations; 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 graph machine learning (GML) problems.
- Proficiency in Python preferred or another major programming language such as C or Java.
- Experience with deep learning libraries and frameworks such as 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).
- Optional experience 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 such as NeurIPS, ICLR, KDD, WWW, AAAI, ICML, or SIGMOD.
- Deep expertise in one or more of the following areas: Graph neural networks (GNNs), graph transformers, 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 and multi-relational knowledge graphs, resource-efficient or federated graph learning, graph-based reasoning and multi-hop inference, 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.
- Must be enrolled in an educational program following the end of the intern assignment.
- Must be a US citizen with the ability to obtain and maintain a US Government Security Clearance.
Benefits & Perks
Compensation range of $43 - $48 per hour, determined by educational year
80 hours of sick time
Assignment length of 8 to 12 weeks with start dates on May 26, June 1, and June 15
One-time stipend for relocation and renting in Malibu, CA
Community-building activities including networking events, movie nights, beach days, and National Intern Day celebrations
Ready to Apply?
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