Applied Researcher I (Multi-agent Systems, Knowledge Graphs/GraphRAG/Graph-of-Thought / GoT, MCP, LangGraph, Agent Protocols)

Capital One • San Jose, CA

4d • $218,700 - $272,300

About The Position

Applied Researcher I (Multi-agent Systems, Knowledge Graphs/GraphRAG/Graph-of-Thought / GoT, MCP, LangGraph, Agent Protocols) Overview: At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, Capital One has 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 and continue our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: 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 work with product, technology and business leaders to apply the state of the art in AI to our business. The AI Foundations – AI Software Engineering team builds scalable, state-of-the-art AI architectures designed to transform the software development lifecycle at Capital One. 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.

Requirements

• Currently has, or is in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with an exception that required degree will be obtained on or before the scheduled start date or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research

Nice To Haves

• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields
• LLM PhD focus on NLP or Masters with 5 years of industrial NLP research experience
• Multiple publications on topics related to the pre-training of large language models (e.g. technical reports of pre-trained LLMs, SSL techniques, model pre-training optimization)
• Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens)
• Publications in deep learning theory
• Publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR
• Agentic AI Research PhD focused on multi-agent systems, autonomous agents, planning, or reinforcement learning
• Hands-on experience developing and deploying multi-agent architectures (e.g., using frameworks like LangGraph or specialized agent protocols)
• Experience with techniques like tool-use integration, memory management for agents, or verifiable agent behavior
• Knowledge Graph Research 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 in designing, implementing, and deploying industrial-scale Knowledge Graph solutions
• Demonstrated expertise with graph databases (e.g., Neo4j, JanusGraph) and graph embedding techniques
• Finetuning PhD focused on topics related to guiding LLMs with further tasks (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning)
• Demonstrated knowledge of principles of transfer learning, model adaptation and model guidance
• Experience deploying a fine-tuned large language model

Responsibilities

• Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products that change how customers interact with their money.
• Leverage a broad stack of technologies — Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more — to reveal the insights hidden within huge volumes of numeric and textual data.
• Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation.
• Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences.
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees