Senior ML Engineer - Production-Scale AI

Capital One National Association; New York (NY); On-site; USD 251,000 - 286,000; Full time

Job summary

Capital One is seeking a Machine Learning Engineer 5 to design, deploy, and scale ML models in production. Ideal candidates bring strong Python/Java/C++ skills, experience with PyTorch or TensorFlow, and proficiency in distributed systems like Spark and Ray.

Cloud and Kubernetes experience is essential for production-grade pipelines. The role involves cross-functional collaboration, building scalable AI platforms, and ensuring responsible AI practices across the ML lifecycle.

Qualifications

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

Responsibilities

• Design, build, and deliver ML models and components with Product/Data Science teams.
• Scale multi-tenant platforms for training and serving ML models at scale.
• Inform ML infrastructure decisions incl. model, data, and feature choices.
• Write and test application code, automate tests and deployment.
• Collaborate in an Agile team to build state-of-the-art ML apps.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

AWS

GCP

Azure

Education

Bachelor's degree in CS or related quantitative field

Master's/PhD preferred

Tools

Kubernetes

Docker

AWS

GCP

Azure

Job description

Capital One is seeking a Machine Learning Engineer 5 to design, deploy, and scale ML models in production. Ideal candidates bring strong Python/Java/C++ skills, experience with PyTorch or TensorFlow, and proficiency in distributed systems like Spark and Ray.

Cloud and Kubernetes experience is essential for production-grade pipelines. The role involves cross-functional collaboration, building scalable AI platforms, and ensuring responsible AI practices across the ML lifecycle.