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