Senior Machine Learning Engineer

Capital One National Association; McLean (VA); On-site; USD 197,000 - 225,000; Full time

Job summary

Capital One National Association seeks a skilled Machine Learning Engineer 4 to design, build, and deploy scalable ML models that solve real business problems. You will work with product and data science teams to select features, train models, and validate results, while ensuring governance and responsible AI practices are followed.

The role emphasizes production-ready pipelines, cloud-based architectures, and CI/CD for ML systems, using Python/Scala/Java and distributed frameworks to deliver

Qualifications

• Bachelor's Degree or higher in Computer Science, Machine Learning or related quantitative field.
• At least 4 years of experience programming with Python, Java, Golang, or C++.
• At least 4 years of ML experience using PyTorch or TensorFlow and libraries (Pandas, NumPy, Scikit-learn).
• At least 4 years using/distributing large scale distributed systems (Spark, Ray).
• At least 2 years deploying ML in production in the cloud (AWS, GCP, Azure) and using Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform ML infra decisions: model choice, data, feature selection, training, tuning, validation.
• Write and test application code, develop/validate ML models, automate tests and deployment.
• Collaborate in a cross-functional Agile team to build scale-ready ML apps.
• Retrain, maintain, and monitor models in production, ensure governance.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

Kubernetes

AWS

GCP

Azure

Education

Bachelor's degree in Computer Science or related

Tools

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

Kubernetes

AWS

GCP

Azure

Job description

Capital One National Association seeks a skilled Machine Learning Engineer 4 to design, build, and deploy scalable ML models that solve real business problems. You will work with product and data science teams to select features, train models, and validate results, while ensuring governance and responsible AI practices are followed.

The role emphasizes production-ready pipelines, cloud-based architectures, and CI/CD for ML systems, using Python/Scala/Java and distributed frameworks to deliver