Capital One
AI Engineer 5 (GenAI Platform, Agentic Infrastructure)
$230K–$286K
San Jose, CA +4 · Full-time · Posted Sep 22, 2026
- Location
- San Jose, CA
San Francisco, 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. 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 optimization of scalable AI infrastructure, including foundation-model training and inference, agentic workflows, and multi-model orchestration for production systems.
What you'll do
- Design, develop, deploy, and support AI components and multi-model orchestration pipelines.
- Develop optimization techniques and lead cost-performance governance for production AI systems.
- Shape technical roadmaps, lead design reviews, and mentor AI engineers.
What they ask for
- Bachelor’s degree in a related field plus at least 6 years of AI/ML algorithm or technology development experience, or a related master’s degree plus at least 4 years.
- At least 6 years programming with Python, Go, Scala, CUDA, or Java.
Nice to have
- Experience deploying scalable, responsible AI solutions on cloud platforms; the posting specifies 7 years for this experience.
- Experience 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 Francisco, CA); $250,800 - $286,200 USD base salary (San Jose, CA)
Tools
AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, Python, Go, Scala, CUDA
AI focus
Builds and optimizes responsible production AI systems, including foundation models, LLM inference, agents, multi-agent workflows, and model orchestration.
Model-written summary · the original posting is authoritative