Capital One
Machine Learning Engineer 4
$179K–$225K
McLean, VA +1 · Full-time · Posted Sep 22, 2026
- Location
- McLean, VA
Plano, TX - Experience
- At least 4 years in programming, ML, and distributed systems; at least 2 years in production ML and cloud operations. Preferred experience ranges from 1+ to 3+ years in specified areas.
- Education
- Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field.
- Visa sponsorship
- Will not sponsor a new applicant for employment authorization or provide immigration-related support.
About the job
Designs, deploys, and operates machine learning models and infrastructure for production applications, including auto-financing technology.
What you'll do
- Build, validate, deploy, retrain, and monitor ML models and components in collaboration with Product and Data Science teams.
- Develop cloud-based ML infrastructure and optimized data pipelines, applying testing, CI/CD, and deployment practices.
- Ensure production ML code and models are secure, well-governed, and aligned with Responsible and Explainable AI practices.
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 ML experience with PyTorch or Tensorflow and libraries including Pandas, NumPy, and Scikit-learn.
- At least 4 years using and operating large-scale distributed systems such as Spark or Ray for AI/ML data preparation.
- At least 2 years deploying and operating production ML solutions and cloud services (AWS, GCP, or Azure), including Kubernetes.
Nice to have
- Master's or Doctoral Degree in a related field.
- Experience optimizing ML algorithms and infrastructure, applying software and production engineering best practices, or leading ML solutions.
Pay
$197,300 - $225,100 USD base salary; $179,400 - $204,700 USD base salary
Tools
Python, Java, Golang, C++, PyTorch, Tensorflow, Pandas, NumPy
AI focus
Build and scale production machine learning models and systems, including model training, optimization, monitoring, and Responsible and Explainable AI.
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