Job Opportunities API

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 scalab