PhD Internship, Applied Research

• Participate in a full-time, 12-week summer applied research experience
• Discover solutions to real-world, large-scale problems
• Conduct high-impact applied research to advance AI-driven customer experiences and contribute to publications
• Partner with applied researchers, data scientists, software engineers, machine learning engineers, and product managers
• Test and design AI-powered products that change how customers interact with their money
• Use PyTorch, AWS Ultra clusters, Hugging Face, Lightning, VectorDBs, and other technologies to analyze large volumes of numeric and textual data
• Translate complex research into tangible business goals
• Collaborate with leading researchers to publish papers at top academic conferences
• Develop professionally through networking, technical deep dives, and executive speaker sessions

• Currently enrolled in an accredited PhD program
• 1st year of PhD coursework must be completed by June 2027
• Located in the continental United States
• In-person attendance at the assigned location
• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related fields
• Programming experience, such as Python
• Experience with at least one deep learning framework, such as Py Torch
• Publications in leading conferences such as ICLR, NeurIPS, ICML, ACL, NAACL, EMNLP, KDD, or CVPR
• Focused research area in foundation models, LLMs, reinforcement learning, causal inference and decision making, sequential user behavior modeling, model inference optimization, interpretability/responsible AI, recommendation systems, uncertainty estimation, or agentic systems
• Capital One will not sponsor a new applicant for employment authorization for this internship position

Demonstrates expertise in AI-driven research and product development, with a strong focus on deep learning frameworks and the ability to translate complex research into actionable business strategies. Proven track record of publishing in leading academic conferences and collaborating with interdisciplinary teams.

• PhD In Computer Science
• Deep Learning Framework Experience
• Publications In Leading Conferences
• Programming Experience In Python
• Research Focus In Foundation Models

• Python
• Deep Learning
• Machine Learning
• Applied Research
• Data Analysis
• Model Inference Optimization
• Recommendation Systems
• Causal Inference
• Reinforcement Learning
• Sequential User Behavior Modeling

• Collaboration
• Networking
• Communication

• AI-Driven Customer Experiences
• Responsible AI
• Interpretability
• Uncertainty Estimation
• Agentic Systems

• Py Torch
• AWS Ultra clusters
• Hugging Face
• Lightning
• VectorDBs