Capital One
AI Engineer 5 ((AI Foundations, LLM Core and Agentic AI)
$230K–$286K
New York, NY +4 · Full-time · Posted Sep 21, 2026
- Location
- New York, NY
San Francisco, CA
McLean, VA
Cambridge, MA
San Jose, CA - Experience
- At least 6 years developing AI/ML algorithms or technologies with a bachelor's degree, or at least 4 years with a master's degree; at least 6 years programming. Preferred: 7 years deploying scalable, responsible AI 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 optimize foundational AI systems, LLMs, and agentic workflows for production use across Capital One.
What you'll do
- Design, develop, deploy, and support AI components, including foundation model training, LLM inference, agents, search, guardrails, evaluation, and observability.
- Optimize large-scale AI systems for scalability, cost, latency, throughput, and hardware utilization.
- Guide technical direction and standards, lead design reviews, and mentor AI engineers.
What they ask for
- Bachelor's degree in a listed or related field plus at least 6 years developing AI/ML algorithms or technologies, or a Master's degree in a listed or related field plus at least 4 years of that experience.
- At least 6 years programming with Python, Go, Scala, CUDA, or Java.
Nice to have
- Experience leading AI system development and making cost, latency, throughput, and accuracy tradeoffs.
- Experience deploying scalable, responsible AI solutions on cloud platforms; the posting specifies 7 years.
Pay
USD $229,900 - $262,400 base salary (Cambridge, MA); USD $229,900 - $262,400 base salary (McLean, VA); USD $250,800 - $286,200 base salary (New York, NY); USD $250,800 - $286,200 base salary (San Francisco, CA); USD $250,800 - $286,200 base salary (San Jose, CA)
Tools
AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, Python, Go, Scala, CUDA
AI focus
Build, deploy, and optimize foundation models, LLM inference, agentic and multi-agent workflows, vector search, model evaluation, and responsible AI governance.
Model-written summary · the original posting is authoritative