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

Capital One • San Jose, CA

3d • $197,300 - $245,600 • Hybrid

About The Position

Capital One is hiring an AI Engineer 4 for its Intelligent Foundations and Experiences (IFX) team, which develops and deploys proprietary AI solutions and platforms used across the company. The role partners with engineers, research scientists, technical program managers, and product managers to build AI-powered products for associates and customers.

The engineer designs, develops, tests, deploys, and supports AI software, including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The role uses open-source and SaaS AI technologies, introduces techniques to optimize production foundation models for scalability, cost, latency, and throughput, and contributes to the long-term technical vision for foundational AI systems.

The position owns end-to-end architecture for complex AI systems, including maintainability, observability, and ethical alignment. It defines and maintains reliability objectives, works with infrastructure engineering to improve GPU/TPU utilization and inference pipelines, and leads technical reviews to ensure security, data governance, and compliance. The role also mentors Principal and Senior Associates on scalable design, performance tuning, and research-to-production work.

This is a hybrid position, with required on-site presence according to Capital One's hybrid policy. The role is eligible for performance-based incentive compensation in addition to salary.

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; or a master's degree in one of these fields and at least 2 years of relevant 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 tradeoffs 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.
• Interest in current 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, agent workflows, similarity search, guardrails, evaluation, experimentation, governance, and observability.
• Use open-source and SaaS AI technologies, such as AWS Ultraclusters, Hugging Face, vector databases, and PyTorch.
• Develop foundation-model optimization techniques to improve production system scalability, cost, latency, and throughput.
• Contribute to the technical vision and long-term roadmap for 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 system deployments, ensuring security, data governance, and compliance standards are met.
• Mentor Principal and Senior Associates on scalable design, performance tuning, and translating research into production.

Benefits

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

What This Job Offers

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