Graph Anomaly Detection Jobs (NOW HIRING)

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.

Position Summary:

   Lead and conduct research in agentic AI, intelligent decision support, autonomous workflows, and LLM-powered agent architectures integrating memory, planning, tool use, and retrieval
   Design, develop, and evaluate multi-agent systems for distributed decision-making, coordination, communication, and longhorizon task execution across mission-critical domains and applications
   Build knowledge-enhanced AI systems that integrate structured knowledge sources, including knowledge graphs, GraphRAG pipelines, ontologies, and multimodal retrieval systems to improve reasoning and context awareness
   Develop and apply graph machine learning (GML) and graph representation learning techniques (e.g., GNNs, geometric deep learning) to support pattern discovery, anomaly detection, and predictive analytics
   Develop trustworthy AI systems, including Explainable AI (XAI), Verification & Validation (V&V), robustness testing, uncertainty quantification, and safety assessments for agentic and graph-based AI systems
   Collaborate with multidisciplinary teams, publish highquality research, support proposal development, and engage with internal and external stakeholders

Required Qualifications:

   Minimum: M.S. in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Network Science, or a related technical field plus 3+ years of relevant industry or research experience in AI/ML
   Strong background in machine learning, deep learning, natural language processing, generative AI, and multimodal foundation models
   Experience adapting and optimizing foundation models through prompt engineering, supervised fine tuning, parameter efficient fine tuning, preference optimization, model alignment, and inference optimization techniques
   Experience developing LLM powered and agentic AI systems using modern agent frameworks (e.g., LangGraph, AutoGen, or equivalent)
   Familiarity with AI interoperability standards and distributed agent architectures, including Model Context Protocol (MCP), Agent2Agent (A2A), or comparable frameworks for tool integration and multi agent communication
   Hands on experience with graph mining, graph matching, geometric deep learning, and applied GML workflows
   Experience with knowledge graphs, ontologies, graph schemas (e.g., LPG, RDF), graph databases (e.g., Neo4j), and graph query languages (e.g., Cypher)
   Proficiency in Python, PyTorch, and modern software engineering practices (version control, testing, collaborative development)
   Experience with large scale data processing and distributed systems (e.g., Ray, Spark), and optionally real time streaming or online learning pipelines
   Experience deploying scalable AI systems using modern LLMOps/AgentOps, distributed inference, GPU acceleration, model serving frameworks (e.g., vLLM, SGLang), observability, and cloud native infrastructure

Preferred Qualifications:

   Ph.D. in a relevant technical discipline with research experience in agentic AI, foundation models, graph machine learning, geometric deep learning, autonomous systems, or related areas
   Prior research publications in top tier AI/ML venues (e.g., NeurIPS, ICML, ICLR, KDD, WWW, AAAI) are highly desirable

Special Requirements:

   U.S. Citizenship with the ability to obtain and maintain a U.S. Government Security Clearance

Compensation and Benefits:

   Pay Range: $140,700 - $175,950 
   Our salary ranges are determined by role, level, and location (California). The range
displayed on each job posting reflects the target range 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.
    Benefits: HRL offers a generous and very competitive total compensation and benefits
package. Our Regular/Full Time benefits include medical, dental, vision, life insurance,
401K match, gym facilities, PTO, Sick time, upward mobility, and an exciting and
challenging work environment.

   For more information about our company benefit offerings please visit:
https://www.hrl.com/careers/benefits

Non-Discrimination and Equal Employment Opportunities (U.S.)
Don't meet every single requirement? Studies have shown that some people are less likely to apply
to jobs unless they meet every single desired qualification. At HRL, we are dedicated to building a
diverse, inclusive, and authentic workplace, so if you're excited about this role but your past
experience doesn't align perfectly with every qualification in the job description, we encourage you
to apply anyway. You may be just the right candidate for this or other roles.
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.

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