Senior ML Engineer — Production & Scale

Capital One; Richmond (VA); On-site; USD 147,000 - 168,000; Full time

Job summary

Capital One is seeking a Machine Learning Engineer to design, build, and deploy production ML models at scale. You will collaborate with product, data science, and engineering teams to deliver end-to-end ML solutions that solve real business problems.

You will work across cloud-based architectures, optimize data pipelines, and ensure models meet governance and reliability standards while following CI/CD and testing best practices.

Qualifications

• Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field.
• 3+ years programming experience with Python, Java, Golang or C++.
• 2+ years ML experience using PyTorch or TensorFlow and libraries (Pandas, NumPy, Scikit-learn).
• 3+ years experience with large-scale distributed systems (Spark, Ray).
• 1+ year deploying ML solutions in production and using Kubernetes to manage large-scale ML software systems in the cloud.

Responsibilities

• Design, build, and deliver ML models and components solving real-world business problems with Product and Data Science teams.
• Inform ML infrastructure decisions including model choice, data and feature selection, training, hyperparameter tuning, and validation.
• Write, test, and deploy application code; develop and validate ML models; automate tests and deployment.
• Collaborate in an Agile team to enable state-of-the-art big data and ML applications.
• Retrain, maintain, and monitor models in production; ensure governance and responsible AI practices.

Skills

Python

Java

Golang

C++

Education

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

Tools

PyTorch

TensorFlow

Spark

Ray

Kubernetes

AWS

GCP

Azure

Job description

Capital One is seeking a Machine Learning Engineer to design, build, and deploy production ML models at scale. You will collaborate with product, data science, and engineering teams to deliver end-to-end ML solutions that solve real business problems.

You will work across cloud-based architectures, optimize data pipelines, and ensure models meet governance and reliability standards while following CI/CD and testing best practices.