Capital One
Machine Learning Engineer 4
$197K–$246K
New York, NY · Full-time · Posted Oct 20, 2026
- Experience
- At least 4 years in programming, ML frameworks/libraries, and large-scale distributed systems; at least 2 years deploying and operating production ML and cloud services.
- 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 not sponsor a new applicant for employment authorization or offer immigration-related support for this position.
About the job
Build, deploy, and operate production machine-learning models and infrastructure at scale to solve business problems.
What you'll do
- Design and deliver ML models and components in collaboration with Product and Data Science teams.
- Develop and operate production ML systems, including cloud architectures, data pipelines, deployment, monitoring, and model retraining.
- Write and test application code and follow responsible, explainable AI and model-governance 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 with large-scale distributed systems for ML data preparation, and at least 2 years deploying production ML in the cloud and managing containerized systems with 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, and leading production ML work.
Pay
$197,300 - $225,100 USD base salary; $215,200 - $245,600 USD base salary
Tools
Python, Java, Golang, C++, PyTorch, Tensorflow, Spark, Ray
AI focus
Build, scale, deploy, monitor, and maintain production ML models and data pipelines; apply responsible and explainable AI practices.
Model-written summary · the original posting is authoritative