Capital One
AI Engineer 4 (AI Foundations)
$197K–$246K
San Jose, CA +2 · Full-time · Posted Oct 14, 2026
- Location
- San Jose, CA
New York, NY
McLean, VA - Experience
- At least 4 years developing AI/ML algorithms or technologies with a bachelor’s degree, or at least 2 years with a master’s degree; at least 4 years programming. Preferred: 6 years deploying AI solutions on cloud platforms.
- Education
- Bachelor’s or master’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields.
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
About the job
Build and operate foundational AI systems and infrastructure that power AI products for Capital One customers and associates.
What you'll do
- Design, develop, deploy, and support AI components, including foundation-model training, LLM inference, agents, similarity search, guardrails, evaluation, governance, and observability.
- Optimize production AI systems for scalability, cost, latency, throughput, and hardware utilization; define reliability objectives and oversee architecture and deployment reviews.
- Partner with cross-functional teams to shape the technical vision and roadmap for foundational AI systems.
What they ask for
- Bachelor’s degree in a related field plus at least 4 years developing AI/ML algorithms or technologies, or a master’s degree in a related field plus at least 2 years of that experience.
- At least 4 years programming with Python, Go, Scala, CUDA, or Java.
Nice to have
- Experience leading AI system development and making tradeoffs among cost, latency, throughput, and accuracy.
- Experience deploying scalable, responsible AI solutions on cloud platforms; the posting specifies 6 years for this qualification.
Pay
$197,300 - $225,100 USD base salary; $215,200 - $245,600 USD base salary
Tools
AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, Python, Go, Scala, CUDA
AI focus
Develop and optimize foundation models, LLM inference, agentic workflows, similarity search, guardrails, model evaluation, and responsible, reliable AI infrastructure.
Model-written summary · the original posting is authoritative