Capital One
Director AI Engineer
$269K–$335K
San Jose, CA +4 · Director · Full-time · Posted Oct 27, 2026
- Location
- San Jose, CA
San Francisco, CA
McLean, VA
Cambridge, MA
New York, NY - Experience
- At least 8 years developing AI/ML algorithms or technologies with a bachelor’s degree, or at least 6 years with a master’s degree; at least 3 years of people leadership experience.
- 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
Leads the engineering strategy, development, and deployment of scalable, responsible AI platforms and products, while building and developing engineering teams.
What you'll do
- Oversee AI components across foundation-model training, LLM inference, search, guardrails, evaluation, governance, and observability.
- Set technical direction and optimize production AI systems for scalability, cost, latency, and throughput; guide build-versus-buy decisions.
- Lead and grow teams, scale engineering practices, and coordinate enterprise AI execution with product, research, compliance, and risk partners.
What they ask for
- Bachelor’s degree in a listed or related field plus at least 8 years developing AI/ML algorithms or technologies, or a master’s degree in a listed or related field plus at least 6 years in that work.
- At least 3 years of people leadership experience.
Nice to have
- 5+ years managing and leading an engineering team; 7+ years deploying scalable, responsible AI solutions on cloud platforms.
- Experience with agentic AI systems, enterprise AI platforms, or AI/ML technologies using languages such as Python, C++, C#, Java, CUDA, or Golang.
Pay
$269,100 - $307,200 USD base salary; $293,600 - $335,100 USD base salary
Tools
AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, AWS, Google Cloud, Azure, Python
AI focus
Develops and operates foundation models and LLM systems, including optimization, inference, similarity search, agentic workflows, guardrails, evaluation, and responsible AI governance.
Model-written summary · the original posting is authoritative