Applied Researcher I (Multi-agent Systems, Knowledge Graphs/GraphRAG/Graph-of-Thought / GoT, MCP, LangGraph, Agent Protocols)
Not Disclosed•Full-TimeOn-site
location_onNYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, we have been leading the industry in using machine learning to create real-time, intelligent, automated customer experiences. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to building world-class applied science and engineering teams to reimagine how we serve our customers and businesses.
The AI Foundations team is at the center of bringing our vision for AI at Capital One to life. Our work touches every aspect of the research life cycle, from partnering with Academia to building production systems. We collaborate with product, technology, and business leaders to apply the state of the art in AI to our business.
Specifically, the AI Foundations – AI Software Engineering team builds scalable, state-of-the-art AI architectures designed to transform the software development lifecycle. Our goal is to empower internal engineers by developing multi-agent solutions that streamline design, code generation, system migration, and troubleshooting to operate software more effectively at scale. To achieve this, we leverage a cutting-edge stack including LangGraph, MCP, Knowledge Graphs, agent-to-agent protocols, and advanced model customization.
As an Applied Researcher I, you will partner with a cross-functional team of data scientists, software engineers, and product managers to deliver AI-powered products that change how customers interact with their money. You will engage in high-impact applied research to take the latest AI developments and push them into the next generation of customer experiences.
This role is for someone who loves the process of analyzing and creating, with a passion for doing the right thing for our customers. You will thrive on bringing definition to big, undefined problems, asking questions, and pushing hard to find answers. You will challenge conventional thinking, work with stakeholders to identify and improve the status quo, and possess the ability to own and pursue a research agenda autonomously.
Capital One is committed to a fair and inclusive hiring process. We consider qualified applicants regardless of background. If you require an accommodation during the application or interview process, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com.
Applications for this role are accepted for a minimum of 5 business days. Please note that no agencies are needed for this position.
Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. We promote a drug-free workplace and will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws.
Work model: On-site
NYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
New York, New York
PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, or Electrical Engineering. LLM focus on NLP or Masters with 5 years of industrial NLP research experience. Multiple publications on pre-training of large language models (e.g., technical reports, SSL techniques, optimization). Membership in a team that trained a large language model from scratch (10B+ parameters, 500B+ tokens). Publications in deep learning theory. Publications at ACL, NAACL, EMNLP, Neurips, ICML, or ICLR. PhD focused on multi-agent systems, autonomous agents, planning, or reinforcement learning. Hands-on experience developing and deploying multi-agent architectures (e.g., using LangGraph or specialized agent protocols). Experience with tool-use integration, memory management for agents, or verifiable agent behavior. PhD focused on knowledge representation and reasoning, graph neural networks (GNNs), or large-scale data integration. Publications in relevant venues (e.g., ISWC, WWW, KDD, Neurips, ICML) on knowledge graph construction, embedding, querying, or reasoning. Experience designing, implementing, and deploying industrial-scale Knowledge Graph solutions. Demonstrated expertise with graph databases (e.g., Neo4j, JanusGraph) and graph embedding techniques. PhD focused on guiding LLMs with further tasks (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning). Demonstrated knowledge of transfer learning, model adaptation, and model guidance. Experience deploying a fine-tuned large language model.
Skills: Multi-Agent Systems, Knowledge Graphs, Graphrag, Graph-Of-Thought, MCP, Langgraph, Agent Protocols, Machine Learning, Pytorch, Aws.
Education: PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields; Master's in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience.
