Agentic AI & Graph Machine Learning Research Engineer

HRL Laboratories, LLC; Calabasas (CA); On-site; USD 128,000 - 160,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Life insurance

401K match

Gym facilities

Paid time off

Job summary

HRL Laboratories is seeking a research scientist to lead AI research on agentic systems, memory-enabled agents, and LLM-powered architectures. You will design multi-agent frameworks, develop graph-based reasoning, and apply GraphRAG and knowledge graphs for robust decision making.

The role focuses on autonomy, rigorous evaluation, and collaboration with cross-disciplinary teams to deliver secure, mission-relevant AI solutions for national security and commercial applications.

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.

Responsibilities

• 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 long-horizon 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 high-quality research, support proposal development, and engage with internal and external stakeholders

Skills

Machine learning

Deep learning

Natural language processing

Generative AI

Multimodal foundation models

Prompt engineering

LLM-powered AI systems

AI interoperability standards

Distributed agent architectures

Graph mining

Graph matching

Geometric deep learning

Knowledge graphs

Ontologies

Graph databases

Cypher

Python

PyTorch

Software engineering practices

Distributed systems

LLMOps/AgentOps

Education

Master’s degree in Computer Science / ML / AI / Applied Mathematics / Network Science or related field

Ph.D. in a relevant technical discipline

Tools

LangGraph

AutoGen

Neo4j

Cypher

Job description

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 long-horizon 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 high-quality 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:$128,000 - $159,950
• 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.

If you would like more information about Equal Employment Opportunity as an applicant under the law, please go to Employees & Job Applicants | U.S. Equal Employment Opportunity Commission

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