Agentic AI & Graph Machine Learning Research Engineer

You will lead the conception and execution of research initiatives that fuse agentic AI with graph machine learning to address mission-critical challenges. You own the design of autonomous workflows that integrate memory, planning, tool use, and retrieval into coherent LLM-powered architectures. You are responsible for developing knowledge-enhanced systems that leverage structured sources such as knowledge graphs and ontologies to elevate reasoning and context awareness. You will architect and evaluate multi-agent systems that enable distributed decision-making, robust coordination, and long-horizon task execution. You drive the creation of trustworthy AI frameworks by embedding Explainable AI, Verification & Validation, and robustness testing into the agent lifecycle. You collaborate with multidisciplinary stakeholders to translate complex requirements into scalable research prototypes and publish findings that influence product direction. You support proposal development and actively engage with both internal teams and external partners to advance the state of the art.

Location: USA

Engagement: Regular

Compensation: $128,000 - $159,950

- Lead research that integrates agentic AI and graph machine learning to solve complex problems in national security and commercial contexts.

- Design and evaluate multi-agent systems that coordinate communication and execute long-horizon tasks across distributed and mission-critical domains.

- Construct knowledge-enhanced AI pipelines that incorporate knowledge graphs, GraphRAG, ontologies, and multimodal retrieval to improve system reasoning.

- Apply graph machine learning and graph representation learning techniques, including GNNs and geometric deep learning, to discover patterns and detect anomalies.

- Develop trustworthy AI systems by implementing Explainable AI, Verification & Validation, robustness testing, and uncertainty quantification for agentic and graph-based models.

- Collaborate with cross-functional teams to translate high-level objectives into research prototypes and support the generation of grant proposals.

- Deploy scalable AI solutions using LLMOps and AgentOps practices, including distributed inference, GPU acceleration, and cloud-native infrastructure.

- Optimize foundation models through prompt engineering, supervised fine-tuning, parameter-efficient fine-tuning, preference optimization, and model alignment.

- Ensure interoperability across distributed agent architectures by applying standards such as Model Context Protocol and Agent2Agent frameworks for tool integration.

- Work with large-scale data processing systems like Ray and Spark, and engage with real-time streaming or online learning pipelines when required.

- Hold a minimum of an M.S. in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Network Science, or a related technical field.

- Bring 3+ years of relevant industry or research experience in AI or machine learning to demonstrate practical implementation skills.

- Show a strong background in machine learning, deep learning, natural language processing, generative AI, and multimodal foundation models.

- Demonstrate experience adapting and optimizing foundation models using prompt engineering, supervised fine-tuning, parameter-efficient fine-tuning, preference optimization, and model alignment.

- Have hands-on experience developing LLM-powered and agentic AI systems using modern frameworks such as LangGraph or AutoGen.

- Exhibit familiarity with AI interoperability standards and distributed agent architectures, including the Model Context Protocol and Agent2Agent protocols.

- Possess hands-on experience with graph mining, graph matching, geometric deep learning, and applied graph machine learning workflows.

- Show experience with knowledge graphs, ontologies, graph schemas such as Labeled Property Graphs and RDF, graph databases like Neo4j, and graph query languages including Cypher.

- Demonstrate proficiency in Python, PyTorch, and modern software engineering practices encompassing version control, testing, and collaborative development.

- Have experience with large-scale data processing and distributed computing frameworks such as Ray and Spark, with optional exposure to real-time streaming or online learning.

- Show experience deploying scalable AI systems using LLMOps and AgentOps, distributed inference, GPU acceleration, model serving frameworks such as vLLM or SGLang, observability tools, and cloud-native infrastructure.

- Hold a Ph.D. in a relevant technical discipline with a research focus on agentic AI, foundation models, graph machine learning, geometric deep learning, or autonomous systems.

- Have prior research publications in top-tier AI and machine learning venues such as NeurIPS, ICML, ICLR, KDD, WWW, or AAAI.

U.S. Citizenship with the ability to obtain and maintain a U.S. Government Security Clearance is required.