Capital One
Machine Learning Engineer 5
$230K–$262K
McLean, VA · Full-time · Posted Oct 19, 2026
- Experience
- At least 6 years programming; at least 6 years Machine Learning; at least 6 years operating large-scale distributed systems; at least 4 years deploying and operating production ML solutions in the cloud and using Kubernetes.
- Education
- Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering).
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
About the job
Build and operate scalable machine-learning infrastructure, models, and production services that support customer credit decisioning across the customer lifecycle.
What you'll do
- Design, build, deploy, retrain, and monitor machine-learning models and components in production.
- Build scalable platforms and data pipelines for model training and serving, using cloud and containerized systems.
- Collaborate with Product, Data Science, and cross-functional teams to deliver reliable, secure, and responsible ML applications.
What they ask for
- Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field.
- At least 6 years of programming experience with Python, Java, Golang, or C++.
- At least 6 years of Machine Learning experience with PyTorch or Tensorflow and libraries including Pandas, NumPy, and Scikit-learn.
- At least 6 years operating large-scale distributed systems for AI/ML data preparation, and at least 4 years deploying production ML solutions in the cloud and using Kubernetes.
Nice to have
- Master's or Doctoral Degree in a related field; 5+ years optimizing ML algorithms, configurations, and infrastructure.
- Experience with software-development and deployment best practices, production data pipelines, ML techniques, or industry contributions.
Pay
$229,900 - $262,400 USD base salary
Tools
Python, Kubernetes, PyTorch, Tensorflow, Pandas, NumPy, Scikit-learn, Spark
AI focus
Build, deploy, scale, retrain, and monitor ML models and infrastructure for credit decisioning; the posting also emphasizes Responsible and Explainable AI.
Model-written summary · the original posting is authoritative