Capital One
AI Engineer 5 (MLX, Agentic AI, Gen AI platform Services)
$230K–$286K
San Jose, CA +4 · Full-time · Posted Oct 20, 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 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 platforms and production systems, including foundation models, LLM inference, agentic workflows, and multi-model orchestration for banking products.
What you'll do
- Design, develop, deploy, and support AI software components and production systems.
- Optimize foundation model training and inference for scalability, cost, latency, throughput, and hardware utilization.
- Contribute to AI platform roadmaps, governance, design reviews, and technical mentorship.
What they ask for
- Bachelor’s degree in a listed or related field plus at least 6 years developing AI/ML algorithms or technologies, or a Master’s degree in a listed or 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 and responsible AI solutions on cloud platforms; 7+ years preferred.
Pay
$229,900 - $262,400 USD base salary (McLean, VA and Cambridge, MA); $250,800 - $286,200 USD base salary (New York, NY, San Francisco, CA, and San Jose, CA)
Tools
AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, Python, Go, Scala, CUDA
AI focus
Develop and optimize foundation models, LLM inference, agentic and multi-agent workflows, similarity search, guardrails, model evaluation, governance, observability, and multi-model orchestration.
Model-written summary · the original posting is authoritative