Job Specifications
Essential Job Functions:
HRL Laboratories, LLC is seeking a Graph Machine Learning Scientist to 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.
Experience Desired:
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, DGL).
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).
Knowledge Desired:
Deep expertise in one or more of the following areas:
Graph neural networks (GNNs) and geometric deep learning o Temporal/dynamic graph learning and event forecasting o 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 Desired: M.S. or Ph.D. in Computer Science, Network Science, Artificial Intelligence, Applied Mathematics, or a closely related discipline.
Special Requirements:
U.S. citizenship is required. Ability to obtain and maintain a DoD security clearance is required.
This is a contract role that will last 18-24 months with the potential to go permanent.
Compensation:
The base hourly range for this position is $58.04 - $72.54 per hour.
Our hourly ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range during the hiring process. Please note that the compensation details listed reflect the base salary only, and do not include potential bonus or benefits.
We are proud to be an EEO/AA employer M/F/D/V. We maintain a drug-free workplace and perform pre-employment substance abuse testing.