Senior Machine Learning Engineer - Scale Production AI

Capital One; Plano (TX); On-site; USD 147,000 - 168,000; Full time

Job summary

Capital One is seeking a Machine Learning Engineer in Plano, TX, to design, build, and deploy ML models that solve real business problems. You will collaborate with product, data science, and operations to scale production models across cloud platforms, using Python, Java, and modern ML frameworks.

The role emphasizes distributed systems (Spark/Ray), containerized deployment with Kubernetes, and end-to-end ML lifecycle including monitoring and governance for responsible AI.

Qualifications

• Bachelor's degree or higher in Computer Science, ML or related quantitative field.
• At least 3 years of experience programming with Python, Java, Golang, or C++.
• At least 2 years of ML experience using PyTorch or TensorFlow and libraries (Pandas, NumPy, Scikit-learn).
• At least 3 years of experience using and operating large-scale distributed systems (Spark, Ray).
• At least 1 year of experience deploying and operating ML solutions in production on 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: model, data, features, training, tuning, and validation.
• Write code, validate models, and automate tests and deployment.
• Collaborate in a cross-functional Agile team to enable state-of-the-art big data and ML apps.
• Retrain, maintain, and monitor models in production.
• Construct optimized data pipelines to feed ML models and feed CI/CD with testing and monitoring.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Spark

Ray

Kubernetes

Education

Bachelor's degree or higher in CS/ML or related field

Master's or PhD preferred

Tools

AWS

GCP

Azure

Job description

Capital One is seeking a Machine Learning Engineer in Plano, TX, to design, build, and deploy ML models that solve real business problems. You will collaborate with product, data science, and operations to scale production models across cloud platforms, using Python, Java, and modern ML frameworks.

The role emphasizes distributed systems (Spark/Ray), containerized deployment with Kubernetes, and end-to-end ML lifecycle including monitoring and governance for responsible AI.