Capital One
AI Engineer 4 (Vision model (VLM) customization experience)
$197K–$246K
New York, NY +3 · Full-time · Posted Sep 24, 2026
- Location
- New York, NY
McLean, VA
Cambridge, MA
San Jose, CA - 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 and responsible AI solutions on cloud platforms.
- Education
- Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields; alternatively, 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 systems and services, from foundation-model development through deployment, reliability, and governance, for Capital One products.
What you'll do
- Design, develop, test, deploy, and support AI components, including model inference, agents, similarity search, guardrails, evaluation, and observability.
- Optimize production AI systems for scalability, cost, latency, throughput, and hardware utilization.
- Own complex system architecture and reliability practices, and lead technical reviews and cross-functional delivery.
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 with cost, latency, throughput, and accuracy tradeoffs.
- Experience deploying scalable, responsible AI solutions on cloud platforms; the posting specifies 6 years.
Pay
$197,300 - $225,100 USD base salary; $215,200 - $245,600 USD base salary
Tools
AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, Python, Go, Scala, CUDA
AI focus
Develop and deploy foundation-model and AI systems, including LLM inference, agents and multi-agent workflows, similarity search, guardrails, and model evaluation; optimize training and inference and support governance and reliability.
Model-written summary · the original posting is authoritative