AI Engineer 4 (AI Foundations: World Model, RL, Causal Inference, Sequential Data)

Capital One • McLean, VA

1d • Hybrid

About The Position

Capital One’s Intelligent Foundations and Experiences (IFX) team builds and deploys proprietary AI models and platforms that help teams across the company deliver responsible, scalable products to millions of customers. The AI Engineer 4 partners with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products for associates and customers.

The role designs, develops, tests, deploys, and supports AI software components, including foundation-model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It uses open-source and SaaS technologies and develops optimization techniques for the performance, scalability, cost, latency, and throughput of production AI systems.

The engineer owns end-to-end architecture for complex AI systems, contributes to the technical vision and roadmap, defines reliability objectives, and collaborates with infrastructure teams to improve accelerator utilization and inference pipelines. The role also leads technical reviews of deployments for security, data governance, and compliance, and mentors Principal and Senior Associates. This is a hybrid position subject to Capital One’s hybrid policy and required on-site presence at the designated office location.

Requirements

• Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field, plus at least 4 years of experience developing AI and ML algorithms or technologies; or a master’s degree in one of these fields or a related field, plus 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.
• 6 or more years of experience deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or an equivalent private cloud.
• Experience designing, developing, delivering, and supporting AI services.
• Experience developing AI and ML algorithms or technologies, such as LLM inference, similarity search and VectorDBs, guardrails, and memory, using Python, C++, C#, Java, CUDA, or Golang.
• Experience developing and applying state-of-the-art techniques to optimize training and inference software for hardware utilization, latency, throughput, and cost.
• Experience building agentic AI systems and workflows.
• Interest in keeping abreast of AI research and systems 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, VectorDBs, and PyTorch.
• Develop and introduce foundation-model optimization techniques to improve scalability, cost, latency, and throughput in production AI systems.
• Contribute to the technical vision and long-term roadmap for foundational AI systems at Capital One.
• 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 system deployments, ensuring security, data governance, and compliance standards are met.
• Mentor Principal and Senior Associates on scalable design, performance tuning, and research-to-production translation.

Benefits

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

What This Job Offers

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