Capital One
AI Engineer 4 (Gen AI Platform Services)
$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 and 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 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 for this position.
About the job
Build and optimize scalable, responsible AI platform services and systems, including foundation-model training, LLM inference, agents, and model evaluation.
What you'll do
- Design, develop, test, deploy, and support AI software components and production AI systems.
- Optimize foundation-model training and inference for scalability, cost, latency, throughput, and hardware utilization.
- Own system architecture and reliability, and guide technical direction and deployment reviews.
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 tradeoffs among cost, latency, throughput, and accuracy.
- Experience deploying scalable, responsible AI solutions on cloud platforms; experience with agentic AI systems, AI services, or training and inference optimization.
Pay
Cambridge, MA: $197,300 - $225,100 USD base salary; McLean, VA: $197,300 - $225,100 USD base salary; New York, NY: $215,200 - $245,600 USD base salary; San Jose, CA: $215,200 - $245,600 USD base salary
Tools
Python, Go, Scala, CUDA, Java, AWS Ultraclusters, Huggingface, PyTorch
AI focus
Develop and operate foundation-model training, large language model inference, agent and multi-agent workflows, similarity search, guardrails, evaluation, governance, and observability; optimize AI systems for production performance and responsible deployment.
Model-written summary · the original posting is authoritative