Capital One
AI Engineer 5 (MLX, Agentic AI, Gen AI platform Services)
$230K–$286K
San Jose, CA +4 · Full-time · Posted Sep 18, 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 cloud AI solutions.
- 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 scalable, production AI systems and platforms, including foundation models, LLM inference, agentic workflows, and multi-model orchestration.
What you'll do
- Design, develop, test, deploy, and support AI components such as model inference, agents, similarity search, guardrails, evaluation, and observability.
- Optimize large-scale AI systems for scalability, cost, latency, throughput, and hardware utilization.
- Contribute to AI platform roadmaps, lead technical design and governance 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 of such experience.
- 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 with scalable cloud AI, agentic systems, model optimization, or integrating heterogeneous AI systems.
Pay
$229,900 - $262,400 USD base salary (McLean, VA; Cambridge, MA); $250,800 - $286,200 USD base salary (New York, NY; San Francisco, CA; 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, evaluation, and multi-model orchestration for production AI systems.
Model-written summary · the original posting is authoritative