Senior Machine Learning Engineer: Scale Production AI

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

Job summary

Capital One National Association is seeking a seasoned Machine Learning Engineer (MLE) to design, build, and productionize ML models across the organization. You will collaborate with product and data science teams to create scalable, multi-tenant platforms for training and serving models, using cloud-native architectures and CI/CD practices.

You will work on end-to-end ML pipelines, retraining and monitoring models in production, and applying responsible AI practices.

Qualifications

• 6+ years of Python/Java/Golang/C++ programming.
• 6+ years ML with PyTorch or TensorFlow and pandas/numpy/sklearn.
• 6+ years using large-scale distributed systems (Spark, Ray).
• 4+ years deploying ML in production on AWS/GCP/Azure with Kubernetes.
• Bachelor's degree or higher in CS or a related quantitative field.

Responsibilities

• Design, build, and deliver ML models to solve real business problems.
• Scale multi-tenant platforms for large footprint ML training/serving.
• Choose data, model and feature strategies; validate results.
• Write and test code, automate tests, and deploy ML solutions.
• Collaborate in an Agile team to create software for big data/ML apps.
• Retrain, monitor, and maintain models in production.
• Leverage cloud architectures to deliver optimized ML models at scale.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

Kubernetes

AWS

GCP

Azure

CI/CD

MLOps

RNNs

Education

Bachelor's degree in CS/related field

Master's degree (preferred)

Doctoral degree (preferred)

Tools

Spark

Ray

Kubernetes

Job description

Capital One National Association is seeking a seasoned Machine Learning Engineer (MLE) to design, build, and productionize ML models across the organization. You will collaborate with product and data science teams to create scalable, multi-tenant platforms for training and serving models, using cloud-native architectures and CI/CD practices.

You will work on end-to-end ML pipelines, retraining and monitoring models in production, and applying responsible AI practices.