Capital One
AI Engineer 4 (MLX, Agentic AI, Gen AI platform Services)
$197K–$246K
San Jose, CA +4 · Full-time · Posted Sep 22, 2026
- Location
- San Jose, CA
San Francisco, CA
McLean, VA
Cambridge, MA
New York, NY - Experience
- At least 4 years developing AI/ML algorithms or technologies with a bachelor’s degree, or at least 2 years with a master’s degree; at least 4 years programming. Preferred: 6 years deploying scalable, responsible AI 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 responsible, scalable AI systems and platform services, including foundation models, inference, and agentic workflows, for Capital One products.
What you'll do
- Design, develop, deploy, and support AI components such as model inference, agents, similarity search, guardrails, evaluation, and observability.
- Optimize production AI systems for scalability, cost, latency, throughput, and hardware utilization; define reliability objectives and oversee system architecture.
- Partner across engineering, research, product, and infrastructure teams on AI system roadmaps, reviews, governance, and deployment.
What they ask for
- Bachelor’s degree in a listed or related field plus at least 4 years developing AI/ML algorithms or technologies, or a master’s degree in a listed or related field plus at least 2 years of that experience.
- At least 4 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 AI on cloud platforms; building agentic systems; optimizing training or inference; or designing distributed AI systems.
Pay
$197,300 - $225,100 USD base salary; $215,200 - $245,600 USD base salary
Tools
Python, Go, Scala, CUDA, Java, AWS Ultraclusters, Huggingface, PyTorch
AI focus
Foundation model training and optimization, LLM inference, agentic and multi-agent workflows, similarity search, guardrails, evaluation, governance, and observability.
Model-written summary · the original posting is authoritative