Capital One
AI Engineer 5
$230K–$286K
San Jose, CA +3 · Full-time · Posted Sep 30, 2026
- Location
- San Jose, CA
McLean, VA
Cambridge, MA
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 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 optimize scalable AI systems and products, including foundation models, LLM inference, agent workflows, and multi-model orchestration for Capital One.
What you'll do
- Design, develop, test, deploy, and support AI software components and production systems.
- Optimize foundation-model training and inference for scalability, cost, latency, throughput, and hardware utilization.
- Guide technical direction, governance, design reviews, and mentoring for AI engineering teams.
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 and making cost, latency, throughput, and accuracy tradeoffs.
- Experience deploying scalable, responsible AI solutions on cloud platforms; 7+ years specified.
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
Develop and optimize foundation models, LLM inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, orchestration, governance, and observability for production AI systems.
Model-written summary · the original posting is authoritative