Senior ML Engineer — Intelligent Foundations & Experiences

Capital One Group; New York, Northern (NY, KY); Hybrid; USD 215,000 - 246,000; Full time

Job summary

Capital One seeks a Machine Learning Engineer IV to advance intelligent foundations and experiences. You will design, implement, and deploy end-to-end ML solutions at scale, collaborating with product, data science, and operations teams across a modern cloud-based stack.

You’ll optimize models, build robust data pipelines, and ensure production readiness with automated testing, monitoring, and governance practices while leveraging open-source frameworks and cutting-edge technologies.

Qualifications

• Bachelor's Degree or higher in Computer Science, ML or related quantitative field.
• 4+ years of programming experience in Python, Java, Golang, or C++.
• 4+ years of ML experience using PyTorch or TensorFlow and libraries (Pandas, NumPy, Scikit-learn).
• 4+ years operating large-scale distributed systems (Spark, Ray).
• 2+ years deploying 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 infra decisions, including model choice, data, and features.
• Train, test, and validate models; automate tests and deployment.
• Collaborate in a cross-functional Agile team for state-of-the-art data and ML apps.
• Retrain, monitor, and maintain models in production; scale pipelines.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Spark

Ray

Cloud (AWS/GCP/Azure)

Keras

Education

Bachelor's degree in Computer Science or related field

Tools

Kubernetes

Docker

CI/CD

Spark

Job description

Capital One seeks a Machine Learning Engineer IV to advance intelligent foundations and experiences. You will design, implement, and deploy end-to-end ML solutions at scale, collaborating with product, data science, and operations teams across a modern cloud-based stack.

You’ll optimize models, build robust data pipelines, and ensure production readiness with automated testing, monitoring, and governance practices while leveraging open-source frameworks and cutting-edge technologies.