Capital One
AI Engineer 5 (AI Foundations, LLM Core and Agentic AI)
$230K–$286K
McLean, VA +3 · Full-time · Posted Sep 21, 2026
- Location
- McLean, VA
Cambridge, MA
San Jose, CA
New York, NY - 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 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
Leads the design and delivery of scalable foundational AI systems, including LLMs and agentic workflows, for Capital One products and platforms.
What you'll do
- Design, develop, deploy, and support AI components such as model training and inference, agent workflows, similarity search, guardrails, evaluation, and observability.
- Optimize production AI systems for scalability, cost, latency, throughput, and hardware utilization; lead cost-performance governance and technical reviews.
- Contribute to foundational AI strategy, coordinate cross-functional delivery, and mentor AI engineers.
What they ask for
- Bachelor’s degree in a related field plus at least 6 years developing AI/ML algorithms or technologies, or a Master’s degree in a related field plus at least 4 years of such experience.
- At least 6 years programming with Python, Go, Scala, CUDA, or Java.
Nice to have
- Experience leading AI system development with tradeoff decisions involving cost, latency, throughput, and accuracy.
- 7 years deploying scalable and responsible AI solutions on cloud platforms.
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
Builds and optimizes foundation models, LLM inference, agentic and multi-agent workflows, similarity search, guardrails, model evaluation, and AI orchestration; emphasizes responsible, scalable production AI.
Model-written summary · the original posting is authoritative