AI Engineer 4

Capital One • San Francisco, CA

20d

About The Position

Capital One is hiring an AI Engineer to build responsible, reliable, and scalable AI systems that improve how associates work and how customers interact with the company. The role is part of the Intelligent Foundations and Experiences (IFX) team, which partners across Capital One to advance AI science and engineering and deliver proprietary AI solutions used across the business.

The engineer will collaborate with engineers, research scientists, technical program managers, and product managers to develop AI-powered products and foundational systems. Work includes designing, developing, testing, deploying, and supporting components such as foundation-model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The role also applies open-source and SaaS AI technologies and develops optimization techniques to improve the scalability, cost, latency, and throughput of production AI systems.

The position contributes to the technical vision and long-term roadmap for foundational AI systems. It owns architecture for complex systems, maintains reliability objectives, collaborates on GPU/TPU utilization and inference performance, and leads technical reviews of deployments to ensure security, data governance, compliance, and ethical alignment. The role also mentors Principal and Senior Associates.

Requirements

• Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field and at least 4 years of experience developing AI/ML algorithms or technologies; alternatively, a master's degree in one of these fields and at least 2 years of such experience.
• At least 4 years of programming experience with Python, Go, Scala, CUDA, or Java.

Nice To Haves

• Experience leading AI-system development and making tradeoff decisions involving cost, latency, throughput, and accuracy.
• At least 6 years of experience deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or equivalent private cloud.
• Experience designing, developing, delivering, and supporting AI services.
• Experience developing AI/ML algorithms or technologies such as LLM inference, similarity search and vector databases, guardrails, or memory using Python, C++, C#, Java, CUDA, or Golang.
• Experience developing and applying state-of-the-art training and inference optimization techniques to improve hardware utilization, latency, throughput, and cost.
• Experience building agentic AI systems and workflows.
• Passion for staying current with AI research and applying novel techniques judiciously in production.
• Proficiency designing distributed systems for model training, evaluation, and online inference at petabyte scale.
• Experience defining AI model-governance processes, including producibility, lineage tracking, and automated retraining schedules.
• Demonstrated ability to influence architectural decisions across multiple AI product lines or platforms.

Responsibilities

• Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products.
• Design, develop, test, deploy, and support AI software components, including foundation-model training, LLM inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
• Use open-source and SaaS AI technologies, including AWS Ultraclusters, Hugging Face, vector databases, and PyTorch.
• Develop foundation-model optimization techniques that improve production-system scalability, cost, latency, and throughput.
• Contribute to the technical vision and long-term roadmap of Capital One's foundational AI systems.
• Own end-to-end architecture for complex AI systems, ensuring maintainability, observability, and ethical alignment.
• Define and maintain service-level objectives for AI reliability, including latency, uptime, and model-performance drift.
• Collaborate with infrastructure engineering to optimize GPU/TPU utilization and accelerate model-inference pipelines.
• Lead cross-functional technical reviews for AI deployments, ensuring security, data governance, and compliance standards are met.
• Mentor Principal and Senior Associates in scalable design, performance tuning, and translating research into production.

Benefits

• Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.
• A comprehensive, competitive, and inclusive set of health, financial, and other benefits supporting total well-being.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior