Senior Machine Learning Engineer - Scale Production AI

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

Job summary

Capital One in Richmond, VA is seeking an experienced Machine Learning Engineer to design, build, and deploy ML models at scale. You will work with Product and Data Science teams to solve real-world business problems and contribute to multi-tenant platforms and production ML pipelines.

The role requires strong Python/Java/Golang, PyTorch or TensorFlow, Spark, Kubernetes, and cloud expertise. Expect collaboration in an Agile team with CI/CD, monitoring, and responsible AI practices.

Qualifications

• Bachelor's degree or higher in Computer Science or related quantitative field.
• 6+ years programming with Python, Java, Golang, or C++.
• 6+ years ML experience with PyTorch or TensorFlow and libraries (Pandas, NumPy, Scikit-learn).
• 6+ years building large-scale distributed systems (Spark, Ray) for AI/ML data.
• 4+ years deploying ML in production on cloud and using Kubernetes for large-scale systems.

Responsibilities

• Design, build, and deliver ML models and components solving real business problems with Product and Data Science teams.
• Scale multi-tenant platforms enabling large footprint ML model training and serving.
• Inform infrastructure decisions with model selection, data, features, and validation.
• Write production-grade code, develop models, and automate tests and deployments.
• Collaborate in a cross-functional Agile team to deliver state-of-the-art ML applications.
• Retrain, monitor, and maintain models in production; ensure governance and Responsible AI.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

Kubernetes

AWS

GCP

Azure

Education

Bachelor's degree in CS or related quantitative field

Master's or Doctorate in CS/EE/Math or related field

Tools

Spark

Ray

Kubernetes

Job description

Capital One in Richmond, VA is seeking an experienced Machine Learning Engineer to design, build, and deploy ML models at scale. You will work with Product and Data Science teams to solve real-world business problems and contribute to multi-tenant platforms and production ML pipelines.

The role requires strong Python/Java/Golang, PyTorch or TensorFlow, Spark, Kubernetes, and cloud expertise. Expect collaboration in an Agile team with CI/CD, monitoring, and responsible AI practices.