Capital One
Machine Learning Engineer 5 (Senior Manager, IC)
$209K–$238K
Chicago, IL · Manager · Full-time · Posted Sep 22, 2026
- Experience
- At least 6 years in programming, ML, and large-scale distributed systems; at least 4 years in production ML and cloud operations. Preferred experience includes multiple areas with 5+ years stated.
- Education
- Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field.
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
About the job
Build, deploy, and operate machine learning models, platforms, and data pipelines that address business problems and run at scale in production.
What you'll do
- Design and deliver ML models, components, and scalable training or serving platforms with Product and Data Science teams.
- Develop application code, models, and data pipelines; automate testing and deployment; retrain, monitor, and maintain production models.
- Use cloud architectures and responsible AI practices to deliver secure, well-governed ML solutions.
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 using PyTorch or Tensorflow and libraries including Pandas, NumPy, and Scikit-learn.
- At least 6 years using and operating large scale distributed systems (Spark, Ray) for AI/ML data preparation, and at least 4 years deploying and operating ML in production and cloud services using Kubernetes.
Nice to have
- Master's or doctoral degree in a related field; 5+ years optimizing ML algorithms, configurations, and infrastructure.
- ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents.
Pay
$209,000 - $238,500 USD base salary
Tools
Python, Java, Golang, C++, PyTorch, Tensorflow, Spark, Ray
AI focus
Build and scale production ML systems and models, including model training and serving, data pipelines, monitoring, and responsible and explainable AI.
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