Senior Machine Learning Engineer – Production & Cloud

Capital One; McLean (VA); On-site; USD 162,000 - 184,000; Full time

Job summary

Capital One is seeking a Machine Learning Engineer to design, build, and deploy ML models solving real business problems. You will collaborate with Product and Data Science teams to select features, train models, and ensure reliable production performance.

You will work in an Agile environment, building data pipelines, retraining models, and applying CI/CD and monitoring practices to ensure robust, scalable ML solutions in the cloud.

Qualifications

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

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform ML infrastructure decisions: model choice, data, feature selection, training, tuning, validation.
• Develop, test, and deploy ML models and application code; automate tests and deployment.
• Collaborate in an Agile team to enable big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage cloud architectures to deliver ML models at scale.
• Create optimized data pipelines to feed ML models.
• Apply CI/CD, test automation, and monitoring for ML deployments.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

Kubernetes

AWS

GCP

Azure

Education

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

Tools

PyTorch

TensorFlow

SAS / Spark

Job description

Capital One is seeking a Machine Learning Engineer to design, build, and deploy ML models solving real business problems. You will collaborate with Product and Data Science teams to select features, train models, and ensure reliable production performance.

You will work in an Agile environment, building data pipelines, retraining models, and applying CI/CD and monitoring practices to ensure robust, scalable ML solutions in the cloud.