Capital One
Full-stack Engineer 4 (Manager, IC)-Risk Tech
$179K–$225K
McLean, VA +2 · Manager · Full-time · Posted Sep 24, 2026
- Location
- McLean, VA
Cambridge, MA
Richmond, VA - Experience
- At least 4 years in software engineering and at least 2 years with public cloud platforms, cloud-based productivity tools, or virtualization technologies. Preferred experience includes 7+ years coding with more than two named languages and other role-specific experience thresholds.
- Education
- Bachelor's Degree or higher in Computer Science or a related quantitative field; preferred Master’s Degree in Computer Science or a related field.
- Visa sponsorship
- Will not sponsor a new applicant for employment authorization or offer immigration-related support for this position.
About the job
Leads engineering teams and projects to build cloud-based, AI-powered risk management solutions that meet regulatory needs.
What you'll do
- Lead technology projects and developers building distributed microservices and full-stack systems for regulatory needs.
- Provide technical leadership, mentor engineers, and set standards for design, development, and operational excellence.
- Partner with product managers and architects to deliver cloud solutions, define service boundaries, and improve system performance.
What they ask for
- Bachelor's Degree or higher in Computer Science or a related quantitative field.
- At least 4 years of experience in software engineering; internship experience does not apply.
- At least 2 years of experience with public cloud platforms, cloud-based productivity tools, or virtualization technologies.
Nice to have
- Master’s Degree in Computer Science or a related field.
- Experience with modern AI-based coding tools or interactive AI tooling beyond basic code completion.
Pay
USD $197,300 - $225,100 base salary (Cambridge, MA); USD $197,300 - $225,100 base salary (McLean, VA); USD $179,400 - $204,700 base salary (Richmond, VA)
Tools
Java, Scala, Python, Go, Node.js, JavaScript, TypeScript, Docker
AI focus
Build and deploy proprietary risk-management solutions powered by AI; the team also uses machine learning to prevent risks and automatically detect issues.
Model-written summary · the original posting is authoritative