About the job
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 delivering 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.
Responsibilities
Join Capital One for a full-time, 12 week, summer applied research experience, discovering solutions to real world, large-scale problems.
Engage in high impact applied research with the goal of taking the latest AI developments and pushing them into the next generation of customer experiences, or contributing to publications in this field.
Partner with a cross-functional team of applied researchers, data scientists, software engineers, machine learning engineers and product managers to test and design 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.
Flex your interpersonal skills to translate the complexity of your work into tangible business goals.
Partner with leading researchers to publish papers at top academic conferences.
Develop professionally through networking sessions, technical deep dives and executive speaker sessions from across Capital One.
Qualifications
Minimum
Currently enrolled in an accredited PhD Program
Completed 2nd year of PhD coursework by program start date (for example: 2nd year of PhD program completed by program start date OR qualifies, at minimum, as a 2nd year in PhD program because of completed Master’s)
Preferred
Completed 3rd or 4th year of PhD Program
PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields
Programming experience (e.g. Python) and experience with at least one deep learning framework (e.g. PyTorch)
Publications in leading conferences such as ICLR, NeurIPs, ICML, ACL, NAACL, EMNLP, KDD, or CVPR
Focused area of research in one of the following areas:
Foundation Models (Language, Vision, Graphs, Time Series), including finetuning and pre-training
LLMs (Agentic AI, Reasoning, Test Time Compute Models, Mixture of Experts)
Reinforcement Learning (World Models, Reasoning, GRPO, PPO, RLHF)
Causal Inference
User Behavior Modeling
Model Inference Optimization
Interpretability, Responsible/Trustworthy AI Models