Capital One
Machine Learning Engineer 4 (Manager, IC)
$197K–$246K
New York, NY +1 · Manager · Full-time · Posted Oct 8, 2026
- Location
- New York, NY
McLean, VA - 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, depending on the qualification.
- 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
- 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 at scale to solve business problems.
What you'll do
- Design and deliver ML models, components, and data pipelines with Product and Data Science teams.
- Develop, test, deploy, monitor, and maintain production ML systems and cloud-based services.
- Apply CI/CD, model governance, and 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 operating large-scale distributed systems such as Spark or Ray to prepare AI/ML data.
- At least 2 years deploying and operating production ML solutions and cloud services (AWS, GCP, Azure), using Kubernetes.
Nice to have
- Master's or Doctoral Degree in a related field.
- Experience optimizing ML systems, building resilient production software and pipelines, leading ML work, or authoring/co-authoring an ML paper.
Pay
USD $197,300 - $225,100 base salary (McLean, VA); USD $215,200 - $245,600 base salary (New York, NY)
Tools
Python, Java, Golang, C++, PyTorch, Tensorflow, Spark, Ray
AI focus
Build and scale production ML solutions, with model training and optimization, monitoring, and responsible and explainable AI practices.
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