AI Engineering Intern

Partner with research scientists, product managers, and engineers to prototype and ship AI-native products Research, prototype, and productionalize high-impact AI systems, including multimodal search, intelligent document processing, and autonomous agentic workflows Design and optimize AI infrastructure, including fine-tuning and pre-training pipelines, reinforcement learning, inference optimization, guardrails, and evaluation frameworks Use AWS Ultraclusters, PyTorch, and agentic frameworks to build AI systems Architect agentic workflows that navigate tools, orchestrate multi-step reasoning, and integrate with complex microservice environments Build Agentic AI applications and platform capabilities for customer-facing GenAI workflows, AI infrastructure, and internal agentic coding tools Contribute to AI systems and AI-native experiences serving Capital One’s 100mm+ customers Requirements Currently has, or is in the process of obtaining, a PhD in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field, with the degree expected by August 2029 or earlier Must continue in the same course of study of the most recent degree after the internship At least 1 year of experience or academic work developing AI and ML algorithms or technologies At least 1 year of experience or academic work programming with Python, Go, Scala, or Java Must be located in the continental United States In-person attendance at the assigned location required for the internship duration Experience or academic work deploying AI/ML systems on cloud platforms such as AWS, Google Cloud, or Azure Experience or academic work developing and applying state-of-the-art techniques for optimizing AI/ML training or inference pipelines, including distributed GPU computational frameworks Experience or academic work with AI/ML domains and systems such as LLM inference, search and retrieval, NLP, computer vision, guardrails, or memory Familiarity with agentic frameworks such as LangChain, LangGraph, LlamaIndex, Autogen,

MCP/A2A

, or frontier-labs agent SDKs Core Competencies Demonstrates expertise in developing and optimizing AI systems, including multimodal search and intelligent document processing, while leveraging cloud platforms like AWS. Proficient in programming languages such as Python and Java, with a strong foundation in AI/ML algorithms and frameworks. Highest-signal resume keywords PhD In Computer Science, AI, Electrical Engineering, Or Related Field AI/ML Algorithm Development Python Programming AWS Deployment Agentic Frameworks Familiarity Hard Skills AI System Prototyping Machine Learning Algorithms Reinforcement Learning Inference Optimization Multimodal Search NLP Computer Vision Distributed GPU Frameworks Fine-Tuning Pipelines Pre-Training Pipelines Industry Keywords AI-Native Products Agentic Workflows Cloud Platforms AI Infrastructure Customer-Facing GenAI Workflows Tools & Technologies AWS Ultraclusters PyTorch LangChain LangGraph LlamaIndex Autogen MCP/A2A Frontier-Labs Agent SDKs #J-18808-Ljbffr Jobtailor