Senior Machine Learning Engineer: AI at Scale

Capital One National Association; Plano (TX); On-site; USD 179,000 - 205,000; Full time

Job summary

Capital One is seeking a Machine Learning Engineer 4 in Plano, TX to design and deploy scalable ML solutions that solve real business problems. You will work with Product and Data Science teams to build models, select features, and optimize performance in production.

The role requires strong experience in Python or Java, plus ML frameworks like PyTorch or TensorFlow, and proficiency with distributed systems, cloud platforms, and Kubernetes to manage large-scale ML software in production.

Qualifications

• Bachelor’s degree or higher in Computer Science, ML or related quantitative field.
• At least 4 years programming in Python, Java, Golang or C++.
• At least 4 years ML experience using PyTorch or TensorFlow and libraries.
• At least 2 years deploying ML solutions in production on cloud platforms and using Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Develop ML infrastructure choices, data pipelines, and model training/evaluation.
• Deploy, monitor, and retrain models in production; ensure CI/CD and security best practices.
• Collaborate in a cross-functional Agile team to deliver scalable ML applications.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

AWS

GCP

Azure

Kubernetes

Education

Bachelor's degree in Computer Science, Electrical Engineering, Mathematics, Statistics, Operations Research or related field

Tools

(none)

Job description

Capital One is seeking a Machine Learning Engineer 4 in Plano, TX to design and deploy scalable ML solutions that solve real business problems. You will work with Product and Data Science teams to build models, select features, and optimize performance in production.

The role requires strong experience in Python or Java, plus ML frameworks like PyTorch or TensorFlow, and proficiency with distributed systems, cloud platforms, and Kubernetes to manage large-scale ML software in production.