Agentic AI & Graph Machine Learning Research Engineer
$128,000–$159,950 year
On-siteCalabasas, California, United States
Job Summary
Lead research in agentic AI, intelligent decision support, and autonomous workflows by designing multi-agent systems for distributed decision-making and long-horizon task execution. Build knowledge-enhanced AI systems integrating structured knowledge sources, including knowledge graphs, GraphRAG pipelines, and multimodal retrieval to improve reasoning. Develop and apply graph machine learning techniques for pattern discovery, anomaly detection, and predictive analytics while establishing trustworthy AI systems with explainability, verification, and safety assessments. Collaborate with multidisciplinary teams to publish high-quality research, support proposal development, and engage with stakeholders on mission-critical domains. Requires U.S. citizenship with security clearance, a Master's or Ph.D. in a technical field, and 3+ years of experience in AI/ML. Salary range $128,000 - $159,950 for a California-based role.
Required Qualifications
- M.S. in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Network Science, or a related technical field
- 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)
- 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
- U.S. Citizenship with the ability to obtain and maintain a U.S. Government Security Clearance
- Must be able to perform pre-employment substance abuse testing
Desired 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)
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