Capital One
Senior Staff Machine Learning Engineer
$286K–$359K
McLean, VA +1 · Senior Staff · Full-time · Posted Sep 29, 2026
- Location
- McLean, VA
Plano, TX - Experience
- At least 10 years programming; at least 8 years in machine learning, distributed systems, and production ML/cloud operations. Preferred experience ranges from 5+ to 9+ years, depending on area.
- Education
- Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field; preferred Master's or Doctoral Degree in a related field.
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
About the job
Leads the development, deployment, and scaling of production machine-learning systems that address financial-services business problems.
What you'll do
- Deliver and evolve ML models and software with product, architecture, engineering, and data science teams; lead large-scale initiatives.
- Deploy optimized models on cloud infrastructure and optimize data pipelines that supply them.
- Promote engineering and modeling best practices and help recruit, develop, and retain engineering talent.
What they ask for
- Bachelor's degree or higher in Computer Science, Machine Learning, or a related quantitative field.
- At least 10 years of programming experience with Python, Java, Golang, or C++.
- At least 8 years of ML experience with PyTorch or Tensorflow and libraries including Pandas, NumPy, and Scikit-learn.
- At least 8 years of experience with distributed systems for ML data preparation, plus production ML deployment and cloud services using Kubernetes.
Nice to have
- Master's or Doctoral Degree in a related field.
- Experience designing large-scale ML systems, optimizing algorithms and infrastructure, shaping ML strategy, and developing engineers.
Pay
$314,800 - $359,300 USD base salary (McLean, VA); $286,200 - $326,700 USD base salary (Plano, TX)
Tools
Python, Scala, Java, GoLang, Dask, RAPIDS, PyTorch, Tensorflow
AI focus
Build and scale production machine-learning models and intelligent systems, including data preparation, training, inference, and cloud deployment.
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