Applied Researcher 5 (AI Foundations: Evaluation and Agentic Workflows)

At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. 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. 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, 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. Lead cross-functional research threads that bridge prototype model development and deployment with ML engineering partners, ensuring scientific insights translate to production impact. The Ideal Candidate You love the process of analyzing and creating, but also share our passion to do the right thing. Innovative. You continually research and evaluate emerging technologies. Creative. You thrive on bringing definition to big, undefined problems. A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. Technical. You’re comfortable with open-source languages and are passionate about developing further. Has a deep understanding of the foundations of AI methodologies. Experience building large deep learning models, whether on language, images, events, or graphs. An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes. Experience in delivering libraries, platform level code or solution level code to existing products. A professional with a track record of coming up with high quality ideas or improving upon existing ideas in machine learning. Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects. Basic Qualifications Currently has, or is in the process of obtaining, 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 plus 2 years of experience in Applied Research or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research. Preferred Qualifications PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields. Behavioral Models: PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series). Multiple papers on topics relevant to training models on graph and sequential data structures at KDD, ICML, NeurIPs, ICLR. Worked on scaling graph models to greater than 50m nodes. Experience with large scale deep learning based recommender systems. Experience with production real-time and streaming environments. Contributions to common open source frameworks (pytorch-geometric, DGL). Proposed new methods for inference or representation learning on graphs or sequences. Worked datasets with 100m+ users. Proven ability to design and lead small-scale research initiatives, guiding junior researchers or engineers through experimental design and evaluation. Salary Information Cambridge, MA: $262,500 - $299,600 for Applied Researcher 5 McLean, VA: $262,500 - $299,600 for Applied Researcher 5