ML Engineer, Foundations for Scalable AI

Capital One Group; San Francisco (CA); On-site; USD 215,000 - 246,000; Full time

Job summary

Capital One is seeking a Machine Learning Engineer 4 to build and scale production ML models, leveraging PyTorch/TensorFlow, Spark, and Kubernetes. You will collaborate with Product, Data Science, and Ops teams to design, train, validate, and deploy solutions that address real business problems.

The role emphasizes scalable data pipelines, model governance, and responsible AI practices in a fast-paced, cross-functional environment.

Qualifications

• Bachelor’s degree or higher in Computer Science, ML or related quantitative field
• 4+ years of Python/Java/Golang/C++
• 4+ years ML experience with PyTorch or TensorFlow
• 4+ years deploying ML in production and operating on cloud platforms (AWS, GCP, Azure) and using Kubernetes
• 3+ years optimizing ML algorithms and building scalable pipelines

Responsibilities

• Design, build, and deliver ML models and components with Product/Data Science teams
• Inform ML infrastructure decisions including model choice, data and feature selection
• Write and test application code, develop and validate ML models, automate tests and deployment
• Collaborate in an Agile team to build software for big data and ML applications
• Retrain, maintain, and monitor models in production
• Leverage cloud architectures to deliver ML models at scale
• Construct data pipelines to feed ML models
• Follow CI/CD and monitoring best practices for deployments
• Ensure governance and Responsible/Explainable AI practices
• Use Python/Scala/Java in development

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Spark

Ray

Kubernetes

Cloud platforms

Education

Bachelor’s Degree or higher in CS/ML or related field

Tools

Spark

Ray

Kubernetes

Job description

Capital One is seeking a Machine Learning Engineer 4 to build and scale production ML models, leveraging PyTorch/TensorFlow, Spark, and Kubernetes. You will collaborate with Product, Data Science, and Ops teams to design, train, validate, and deploy solutions that address real business problems.

The role emphasizes scalable data pipelines, model governance, and responsible AI practices in a fast-paced, cross-functional environment.