Senior Machine Learning Engineer - Scale Production AI

capitalone; Chicago (IL); On-site; USD 179,000 - 205,000; Full time

Job summary

Capital One in Chicago, IL seeks a Machine Learning Engineer 4 to design, build and deploy scalable ML models that solve real business problems. You will collaborate with product, data science, and operations teams to deliver impactful AI solutions in production environments.

You'll work with open source frameworks, distributed systems, cloud platforms, and CI/CD practices to ensure reliability, governance, and explainability of models at scale.

Qualifications

• Bachelor's degree or higher in Computer Science, ML or related quantitative field.
• 4+ years of Python/Java/Golang/C++ programming experience.
• 4+ years ML experience with PyTorch or TensorFlow.
• 4+ years operating large-scale distributed systems (Spark, Ray).
• 2+ years deploying ML in production on cloud (AWS, GCP, Azure) and using Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components for real-world business problems.
• Inform ML infrastructure choices, data/feature selection, training, and validation.
• Write and test application code, develop/validate models, and automate tests/deployment.
• Collaborate in an Agile cross-functional team to enable scalable ML apps.
• Retrain, maintain, and monitor models in production; ensure governance and explainability.
• Leverage cloud architectures to deliver optimized ML models at scale.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Spark

Kubernetes

AWS

Education

Bachelor's degree in Computer Science or related field

Tools

Docker

GCP

Azure

CI/CD tooling

Job description

Capital One in Chicago, IL seeks a Machine Learning Engineer 4 to design, build and deploy scalable ML models that solve real business problems. You will collaborate with product, data science, and operations teams to deliver impactful AI solutions in production environments.

You'll work with open source frameworks, distributed systems, cloud platforms, and CI/CD practices to ensure reliability, governance, and explainability of models at scale.