Sr Staff Applied Researcher

Capital One•McLean, VA

7d•Remote

About The Position

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. This is an individual contributor (IC) role driving strategic direction through collaboration with Applied Science, Engineering and Product leaders across Capital One. As a well-respected IC leader, you will guide and mentor a team of applied scientists and their managers without being a direct people leader. You will be expected to be an external leader representing Capital One in the research community, collaborating with prominent faculty members in the relevant AI research community.

Requirements

PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 6 years of experience in Applied Research or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 8 years of experience in Applied Research
Has a deep understanding of the foundations of AI methodologies
Experience building large deep learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF
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, demonstrated by accomplishments such as first author publications or projects
Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects

Nice To Haves

PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series)
Member of technical leadership for model deployment for a very large user behavior model
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
Recognized authority in AI or ML research with demonstrated influence on the evolution of a subfield (e.g., foundation models, multimodal learning, reasoning systems, or model efficiency)

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.
Define and evolve the company’s long-term research agenda - identifying emergent scientific opportunities and steering foundational investments in AI innovation