Capital One
Director, AI Engineering (Remote - eligible)
$245K–$335K
McLean, VA +3 · Director · Full-time · Posted Oct 9, 2026
- Location
- McLean, VA
San Francisco, CA
US Remote
New York, NY - Experience
- At least 8 years developing AI and 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. Preferred: 5+ years managing and leading an engineering team and 7+ 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
Leads teams building and scaling responsible AI systems and platforms, translating enterprise strategy into AI products and infrastructure for Capital One.
What you'll do
- Lead design, development, deployment, and support of AI systems, including foundation models, LLM inference, search, guardrails, evaluation, and governance.
- Set technical vision and roadmap; optimize production AI systems and scale shared engineering practices across teams.
- Lead and develop AI talent, coordinate cross-functional execution, and establish responsible AI standards with risk and compliance partners.
What they ask for
- Bachelor’s degree in a related field plus at least 8 years developing AI/ML algorithms or technologies, or a master’s degree in a related field plus at least 6 years.
- At least 3 years of people leadership experience.
Nice to have
- 5+ years managing and leading an engineering team; 7+ years deploying scalable and responsible AI solutions on cloud platforms.
- Experience with agentic AI systems and workflows, or AI/ML technologies using Python, C++, C#, Java, CUDA, or Golang.
Pay
$244,700 - $279,200 USD base salary; $269,100 - $307,200 USD base salary; $293,600 - $335,100 USD base salary; $293,600 - $335,100 USD base salary
Tools
AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, AWS, Google Cloud, Azure, Python
AI focus
Leads development and optimization of foundation models, LLM inference, similarity search, guardrails, model evaluation, agentic AI, responsible AI governance, and scalable AI platforms.
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