Senior ML Engineer: Production-Scale AI

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

Job summary

Capital One is seeking a Machine Learning Engineer 4 to design, build, and deploy scalable ML models in a fast-paced, cross-functional environment. You will collaborate with Product, Data Science, and Ops to deliver production-ready AI solutions using PyTorch/TensorFlow and distributed systems like Spark and Kubernetes.

You will work with cloud platforms (AWS/GCP/Azure), build data pipelines, and ensure model governance and explainability.

Qualifications

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

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform ML infrastructure decisions including model choice, data, and features.
• Develop and validate ML models; automate tests and deployment.
• Collaborate in a cross-functional Agile team to enable big data and ML apps.
• Retrain, maintain, and monitor models in production.
• Leverage or build cloud-based architectures to scale ML models.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

Kubernetes

Cloud (AWS/GCP/Azure)

Education

Bachelor's degree or higher in CS/ML/related field

Master's or PhD preferred

Tools

Spark

Kubernetes

Cloud platforms (AWS, GCP, Azure)

Job description

Capital One is seeking a Machine Learning Engineer 4 to design, build, and deploy scalable ML models in a fast-paced, cross-functional environment. You will collaborate with Product, Data Science, and Ops to deliver production-ready AI solutions using PyTorch/TensorFlow and distributed systems like Spark and Kubernetes.

You will work with cloud platforms (AWS/GCP/Azure), build data pipelines, and ensure model governance and explainability.