Capital One
AI Engineer 4
$197K–$246K
San Francisco, CA +4 · Full-time · Posted Oct 19, 2026
- Location
- San Francisco, CA
McLean, VA
Cambridge, MA
San Jose, CA
New York, NY - 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 lead scalable AI systems and products, including foundation models, inference, agents, and supporting infrastructure, for Capital One.
What you'll do
- Design, develop, test, deploy, and support AI software components and production systems.
- Optimize foundation model training and inference for scalability, cost, latency, throughput, and hardware utilization.
- Own AI system architecture and reliability, and lead technical reviews covering security, governance, and compliance.
What they ask for
- Bachelor's degree in a related field plus at least 4 years of experience developing AI and ML algorithms or technologies, or a Master's degree in a related field plus at least 2 years of that experience.
- At least 4 years of programming experience with Python, Go, Scala, CUDA, or Java.
Nice to have
- Experience leading AI system development and making tradeoff decisions involving cost, latency, throughput, and accuracy.
- 6+ years of experience deploying scalable and responsible AI solutions on cloud platforms.
Pay
$197,300 - $225,100 USD base salary (Cambridge, MA and McLean, VA); $215,200 - $245,600 USD base salary (New York, NY, San Francisco, CA, and San Jose, CA)
Tools
AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, Python, Go, Scala, CUDA
AI focus
Develop production AI products and foundational systems involving foundation model training, LLM inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, governance, and optimization.
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