Senior ML Engineer - Production-Scale AI Systems

Capital One National Association; New York (NY); On-site; USD 215,000 - 246,000; Full time

Job summary

Capital One National Association in New York, NY is seeking a senior Machine Learning Engineer to design, implement, and scale ML models and data pipelines. You will collaborate with Product and Data Science teams to deliver production-ready AI solutions and ensure governance and reliability across systems.

The role emphasizes distributed systems, cloud deployment, and modern ML tooling with a focus on responsible and explainable AI practices.

Qualifications

• Bachelor's degree or higher in Computer Science or a related quantitative field.
• At least 4 years of Python, Java, Golang, or C++ programming experience.
• At least 4 years of ML experience with PyTorch or TensorFlow and Pandas/NumPy/Scikit-learn.
• At least 4 years operating large scale distributed systems (Spark, Ray).
• At least 2 years deploying ML solutions in production in the cloud (AWS/GCP/Azure) and using Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components with cross-functional teams.
• Inform infrastructure choices with ML modeling techniques and data selection.
• Write production-ready code, validate models, and automate tests/deployments.
• Collaborate in an Agile team to enable scalable big data/ML apps.
• Retrain, monitor, and maintain models in production and scale pipelines.

Skills

ML engineering

Distributed systems

Cloud deployment

CI/CD

Education

Bachelor's degree or higher in Computer Science or related field

Master's degree preferred

Tools

Python

Java

Golang

C++

PyTorch

TensorFlow

Spark

Ray

Kubernetes

AWS

GCP

Azure

Job description

Capital One National Association in New York, NY is seeking a senior Machine Learning Engineer to design, implement, and scale ML models and data pipelines. You will collaborate with Product and Data Science teams to deliver production-ready AI solutions and ensure governance and reliability across systems.

The role emphasizes distributed systems, cloud deployment, and modern ML tooling with a focus on responsible and explainable AI practices.