Machine Learning Engineer — Scale Production AI

Capital One; Richmond (VA); On-site; USD 179,000 - 205,000; Full time

Job summary

Capital One is seeking a Machine Learning Engineer to design, build, and deploy production-grade ML models across diverse business domains. You will collaborate with product and data science teams to create scalable ML solutions, optimize infrastructure, and ensure governance and responsible AI practices.

The role emphasizes cloud-based architectures, CI/CD, and cross-functional collaboration within an Agile framework.

Qualifications

• Bachelor’s degree or higher in Computer Science, ML or related quantitative field
• At least 4 years of experience programming with Python, Java, Golang, or C++
• At least 4 years of Machine Learning experience using PyTorch or TensorFlow
• At least 4 years of experience using and operating large scale distributed systems (Spark, Ray)
• At least 2 years deploying and operating ML solutions 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 infrastructure decisions including model choice, data, and feature selection
• Write and test application code, develop/validate ML models, and automate tests & deployment
• Collaborate in cross-functional Agile teams to enable big data and ML apps
• Retrain, maintain, and monitor models in production and ensure governance and responsible AI

Skills

Python

Java

SQL

ML frameworks

PyTorch

TensorFlow

Spark

Cloud & DevOps

Education

Bachelor’s degree in CS or related field

Tools

Spark

Kubernetes

Docker

Airflow

Job description

Capital One is seeking a Machine Learning Engineer to design, build, and deploy production-grade ML models across diverse business domains. You will collaborate with product and data science teams to create scalable ML solutions, optimize infrastructure, and ensure governance and responsible AI practices.

The role emphasizes cloud-based architectures, CI/CD, and cross-functional collaboration within an Agile framework.