Senior ML Engineer, AI Risk Tech: Scale Production Models

Capital One National Association; Richmond (VA); On-site; USD 209,000 - 239,000; Full time

Job summary

Capital One is seeking a Machine Learning Engineer 5 (Senior Manager IC) in Risk Tech. You will design, deploy, and scale ML models to manage risk, collaborating with Product, Data Science, and Ops teams while leveraging cloud platforms and modern ML tooling.

You will build robust data pipelines, ensure model governance and responsible AI practices, and contribute to large-scale production ML systems across AWS/GCP/Azure environments.

Qualifications

• Bachelor's or higher in CS or related quantitative field.
• At least 6 years of experience programming with Python, Java, Golang, or C++.
• At least 6 years of ML experience using PyTorch or TensorFlow and libraries (Pandas, NumPy, Scikit-learn).
• At least 6 years of experience with large scale distributed systems (Spark, Ray).
• At least 4 years deploying and operating ML solutions in production in the cloud and using Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components solving real-world problems with Product and Data Science teams.
• Scale multi-tenant platforms enabling large footprint ML training and serving.
• Inform infrastructure decisions with modeling techniques and data selection considerations.
• Write and test application code, validate models, and automate deployment and testing.
• Collaborate in a cross-functional Agile team to enable state-of-the-art ML apps.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Scikit-learn

Spark

Kubernetes

AWS

GCP

Azure

Education

Bachelor's Degree or higher in Computer Science or related quantitative field

Tools

Spark

Ray

Kubernetes

AWS

GCP

Azure

Job description

Capital One is seeking a Machine Learning Engineer 5 (Senior Manager IC) in Risk Tech. You will design, deploy, and scale ML models to manage risk, collaborating with Product, Data Science, and Ops teams while leveraging cloud platforms and modern ML tooling.

You will build robust data pipelines, ensure model governance and responsible AI practices, and contribute to large-scale production ML systems across AWS/GCP/Azure environments.