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San Jose, CA

Internship

Hybrid

$191K/yr - $209K/yr

Intern

Capital One is a leading financial services company focused on creating trustworthy and reliable AI systems. They are seeking a PhD intern for a summer applied research experience, where the intern will engage in high-impact applied research and collaborate with cross-functional teams to design AI-powered products that enhance customer interactions.

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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

Qualification

Required

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