Capital One
Machine Learning Engineer 4
$197K–$225K
McLean, VA · Full-time · Posted Sep 21, 2026
- Experience
- At least 4 years programming, 4 years machine learning, and 4 years operating large-scale distributed systems; at least 2 years deploying and operating production ML solutions in the cloud and using Kubernetes. Preferred experience ranges from 1+ to 3+ years, depending on the qualification.
- Education
- Bachelor's degree or higher in Computer Science, Machine Learning, or a related quantitative field; a Master's or doctoral degree is preferred.
- Visa sponsorship
- Capital One will not sponsor a new applicant for employment authorization or provide immigration-related support for this position.
About the job
Build, deploy, and operate production machine learning models and infrastructure that address business problems and support AI applications at scale.
What you'll do
- Design and deliver ML models, components, and data pipelines in collaboration with Product and Data Science teams.
- Develop, test, deploy, retrain, maintain, and monitor production ML systems using cloud platforms and CI/CD practices.
- Apply responsible and explainable AI practices and manage model governance and code vulnerabilities.
What they ask for
- Bachelor's degree or higher in Computer Science, Machine Learning, or a related quantitative field.
- At least 4 years programming with Python, Java, Golang, or C++.
- At least 4 years of machine learning experience with PyTorch or Tensorflow and libraries including Pandas, NumPy, and Scikit-learn.
- At least 4 years operating large-scale distributed systems for AI/ML data preparation, plus at least 2 years deploying production ML solutions in the cloud and using Kubernetes.
Nice to have
- Master's or doctoral degree in a related field.
- Experience optimizing ML algorithms and infrastructure, applying software development and deployment best practices, or leading ML solutions.
Pay
USD $197,300 - $225,100 base salary
Tools
Python, Java, Golang, C++, PyTorch, Tensorflow, Spark, Ray
AI focus
Build and scale production ML models; optimize model infrastructure and pipelines; apply responsible and explainable AI and model governance.
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