Graph ML & AI Research Engineer — Agentic LLMs

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 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.