Capital One
AI Engineer 5 (AI Foundations, LLM Core and Agentic AI)
$230K–$286K
Cambridge, MA +3 · Full-time · Posted Sep 18, 2026
- Location
- Cambridge, MA
McLean, VA
San Jose, CA
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. Preferred: 7 years deploying AI solutions on cloud platforms.
- 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 lead foundational AI systems, LLM and agentic workflows, and scalable production infrastructure for Capital One products.
What you'll do
- Design, develop, deploy, and support AI components, including foundation model training, LLM inference, agents, search, guardrails, evaluation, and observability.
- Develop optimization techniques and multi-model orchestration pipelines to improve AI system performance, cost, latency, and throughput.
- Guide technical direction and governance, lead design reviews, and mentor AI engineers.
What they ask for
- Bachelor’s degree in a related field plus at least 6 years of experience developing AI and ML algorithms or technologies, or a Master’s degree in a related field plus at least 4 years of that experience.
- At least 6 years of programming experience with Python, Go, Scala, CUDA, or Java.
Nice to have
- Experience leading AI system development and making cost, latency, throughput, and accuracy tradeoffs.
- Experience with scalable cloud AI, agentic workflows, AI optimization, and integrating heterogeneous AI systems.
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
AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, Python, Go, Scala, CUDA
AI focus
Develop foundation models, LLM inference, agentic and multi-agent workflows, similarity search, guardrails, and model evaluation; optimize and govern production AI systems.
Model-written summary · the original posting is authoritative