Senior Machine Learning Engineer — Production-Scale AI

Capital One; New York (NY); On-site; USD 140,000 - 190,000; Full time

Job summary

Capital One is hiring a Machine Learning Engineer to design, build, and productionize ML models in collaboration with Product and Data Science teams. You will tackle scalable data pipelines, model governance, and cloud-based deployment across AWS, GCP, or Azure.

The role emphasizes responsible AI practices, cross-functional Agile work, and delivering production-grade ML solutions with modern tooling and CI/CD workflows.

Qualifications

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

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform ML infrastructure choices, including model/data/feature selection and validation.
• Write production-grade code, validate models, and automate tests and deployment.
• Collaborate in Agile cross-functional teams to enable big data and ML apps.
• Retrain, maintain, and monitor models in production.
• Leverage cloud architectures to deliver ML models at scale.
• Construct data pipelines for feeding ML models.
• Follow CI/CD with testing and monitoring; ensure governance and responsible AI.
• Use Python, Scala, or Java.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

Kubernetes

Docker

AWS

GCP

Azure

Education

Bachelor's degree in Computer Science, Machine Learning or related field

Tools

Spark

Ray

Kubernetes

Docker

AWS

GCP

Azure

Job description

Capital One is hiring a Machine Learning Engineer to design, build, and productionize ML models in collaboration with Product and Data Science teams. You will tackle scalable data pipelines, model governance, and cloud-based deployment across AWS, GCP, or Azure.

The role emphasizes responsible AI practices, cross-functional Agile work, and delivering production-grade ML solutions with modern tooling and CI/CD workflows.