Capital One

AI Engineer 5 (AI Foundations, LLM Core and Agentic AI)

$230K–$286K

San Jose, CA +3 · Full-time · Posted Sep 18, 2026

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Location: San Jose, CA
McLean, VA
Cambridge, MA
New York, NY
Experience: At least 6 years developing AI and ML algorithms or technologies with a bachelor's degree, or at least 4 years with a master's degree; at least 6 years programming.
Education: Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields; or a master's degree in those fields.
Visa sponsorship: Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

About the job

Build and optimize foundational AI systems, LLM and agentic workflows, and production infrastructure for Capital One products.

What you'll do

Design, develop, deploy, and support AI components including foundation model training, LLM inference, agents, similarity search, guardrails, evaluation, and observability.
Optimize large-scale AI systems for scalability, cost, latency, throughput, and hardware utilization; build multi-model orchestration pipelines.
Set technical direction and governance, lead design reviews, and mentor AI engineers.

What they ask for

Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies; or a Master's degree in those fields plus at least 4 years of that experience.
At least 6 years of experience programming with Python, Go, Scala, CUDA, or Java.

Nice to have

Experience leading development of AI systems with cost, latency, throughput, and accuracy tradeoffs.
Experience deploying scalable and responsible AI solutions on cloud platforms, and building agentic AI systems and workflows.

Pay

$229,900 - $262,400 USD base salary (Cambridge, MA); $229,900 - $262,400 USD base salary (McLean, VA); $250,800 - $286,200 USD base salary (New York, NY); $250,800 - $286,200 USD base salary (San Jose, CA)

Tools

Python, Go, Scala, CUDA, Java, AWS Ultraclusters, Huggingface, PyTorch

AI focus

Develop and optimize responsible, scalable foundation-model and LLM infrastructure, inference, agentic workflows, similarity search, guardrails, evaluation, and multi-model orchestration.

Model-written summary · the original posting is authoritative