Senior ML Engineer, Scale & Production AI

Capital One Group; New York (NY); On-site; USD 251,000 - 286,000; Full time

Job summary

Capital One is seeking a senior Machine Learning Engineer to design, build, and deploy scalable ML models for production across cloud platforms. The role collaborates with Ops, Data Science, and Engineering to deliver end-to-end ML solutions.

Expect responsibilities across model training, deployment, monitoring, and continuous improvement, with strong emphasis on responsible AI and governance in a large-scale financial tech environment.

Qualifications

• Bachelor's degree or higher in CS, ML or related quantitative field.
• 6+ years of Python/Java/Golang coding.
• 6+ years ML with PyTorch/TensorFlow.
• 6+ years distributed systems Spark/Ray.
• 4+ years deploying ML in production on AWS/GCP/Azure with Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components with the Product and Data Science teams.
• Build and scale multi-tenant platforms for training and serving ML models at scale.
• Inform ML infrastructure decisions including data, features, and model selection.
• Collaborate in a cross-functional Agile team to create software for big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage cloud-based architectures to deploy optimized ML models at scale.
• Construct optimized data pipelines for ML models.
• Implement CI/CD and test automation for ML models and apps.

Skills

Python

Java

Golang

PyTorch

TensorFlow

Spark

Ray

Kubernetes

AWS

GCP

Azure

CI/CD

Responsible AI

Explainable AI

Education

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

Tools

Docker

Job description

Capital One is seeking a senior Machine Learning Engineer to design, build, and deploy scalable ML models for production across cloud platforms. The role collaborates with Ops, Data Science, and Engineering to deliver end-to-end ML solutions.

Expect responsibilities across model training, deployment, monitoring, and continuous improvement, with strong emphasis on responsible AI and governance in a large-scale financial tech environment.