Capital One
AI Engineer 5
$230K–$286K
San Jose, CA +3 · Full-time · Posted Sep 18, 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 scalable and responsible 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
Build and optimize scalable AI systems and products, including foundation models, LLM inference, agents, search, and orchestration, for Capital One.
What you'll do
- Design, develop, test, deploy, and support AI software and multi-model orchestration systems.
- Optimize production AI systems for scalability, cost, latency, throughput, and hardware utilization; establish cost-performance governance.
- Shape technical roadmaps and standards, lead design reviews, 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 in that work.
- 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 stated.
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 foundation model training and LLM inference, agentic and multi-agent workflows, similarity search, guardrails, evaluation, governance, observability, and AI system optimization.
Model-written summary · the original posting is authoritative