Capital One
Machine Learning Engineer 4
$179K–$205K
Chicago, IL · Full-time · Posted Sep 21, 2026
- Experience
- 4+ years programming and ML experience; 4+ years operating large-scale distributed systems; 2+ years production ML and cloud operations.
- 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
Build, deploy, and operate production machine-learning models and infrastructure to solve business problems and support AI applications at scale.
What you'll do
- Design, build, test, and deploy ML models, application code, data pipelines, and cloud-based infrastructure.
- Retrain, maintain, and monitor production models and services, applying CI/CD and deployment best practices.
- Collaborate with Product, Data Science, and cross-functional Agile teams; promote model governance and Responsible and Explainable AI.
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 to prepare AI/ML data.
- At least 2 years deploying and operating ML solutions in production and cloud production services (AWS, GCP, or Azure), using Kubernetes for large-scale containerized ML systems.
Nice to have
- Master's or doctoral degree in a related field; 3+ years optimizing ML algorithms, configurations, and infrastructure.
- Experience with software development best practices, resilient production systems, ML techniques and architectures, production data pipelines, technical leadership, or authorship of an ML paper.
Pay
$179,400 - $204,700 USD base salary (Chicago, IL)
Tools
Python, Java, Golang, C++, PyTorch, Tensorflow, Spark, Ray
AI focus
Develop, deploy, scale, and operate production ML models; apply model training and evaluation, optimization, monitoring, governance, and Responsible and Explainable AI practices.
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