Senior Machine Learning Engineer — Scale Production AI

Capital One National Association; Chicago (IL); On-site; USD 179,000 - 205,000; Full time

Job summary

Capital One National Association is seeking a Machine Learning Engineer 4 in Chicago, IL to design, build, and productionize ML models that solve real business problems. You’ll collaborate with Product, Data Science, and Ops teams to deliver scalable AI solutions, harnessing open source frameworks and cloud infrastructure.

The role emphasizes responsible AI, model monitoring, and CI/CD for ML systems, using Python, Java, or Go, and deploying on AWS/GCP/Azure with Kubernetes.

Qualifications

• Bachelor's degree or higher in CS, ML or related quantitative field.
• At least 4 years of programming experience in Python, Java, Golang, or C++.
• At least 4 years ML experience with PyTorch or TensorFlow.
• At least 4 years operating large scale distributed systems (Spark, Ray).
• At least 2 years deploying ML in production and using Kubernetes in cloud.

Responsibilities

• Design, build, and deliver ML models and components for real-world business problems.
• Inform ML infrastructure decisions: data, model, training, tuning and validation.
• Write production code, validate models, and automate tests and deployment.
• Collaborate in cross-functional Agile teams to enable scalable ML apps.
• Retrain, monitor, and maintain models in production.
• Leverage cloud architectures to deliver optimized models at scale.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Spark

Ray

Kubernetes

AWS

GCP

Azure

Education

Bachelor's degree in CS/ML or related quantitative field

Master's or PhD preferred

Tools

CI/CD

Git

Docker

Job description

Capital One National Association is seeking a Machine Learning Engineer 4 in Chicago, IL to design, build, and productionize ML models that solve real business problems. You’ll collaborate with Product, Data Science, and Ops teams to deliver scalable AI solutions, harnessing open source frameworks and cloud infrastructure.

The role emphasizes responsible AI, model monitoring, and CI/CD for ML systems, using Python, Java, or Go, and deploying on AWS/GCP/Azure with Kubernetes.