Capital One
AI Engineer 5 (MLX, Agentic AI, Gen AI platform Services)
$230K–$286K
San Jose, CA +4 · Full-time · Posted Oct 16, 2026
- Location
- San Jose, CA
San Francisco, CA
McLean, VA
Cambridge, MA
New York, NY - Experience
- 6 years programming; AI/ML development: 6 years with a bachelor’s degree or 4 years with a master’s degree. 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.
About the job
Build and optimize foundational AI systems and agentic workflows that power customer and associate products across Capital One.
What you'll do
- Design, deploy, and support AI components, including foundation-model training, LLM inference, agents, search, guardrails, evaluation, and observability.
- Optimize large-scale AI systems for performance, scalability, cost, latency, and throughput; guide orchestration and cost-performance governance.
- Contribute to technical roadmaps and design standards, lead reviews, and mentor AI engineers.
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 doing so.
- At least 6 years programming with Python, Go, Scala, CUDA, or Java.
Nice to have
- Experience leading AI system development with cost, latency, throughput, and accuracy tradeoffs.
- Experience deploying scalable, responsible AI solutions on cloud platforms; 7+ years preferred.
Pay
$229,900 - $262,400 USD base salary (McLean, VA); $229,900 - $262,400 USD base salary (Cambridge, MA); $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
Python, Go, Scala, CUDA, Java, AWS Ultraclusters, Huggingface, PyTorch
AI focus
Foundation models, LLM inference, agentic and multi-agent workflows, similarity search, vector databases, guardrails, model evaluation, and optimization of AI training and inference.
Model-written summary · the original posting is authoritative