Senior ML Engineer, MLX: Scalable Production AI

Capital One; United States; Remote; USD 179,000 - 246,000; Full time

Job summary

Capital One is seeking a Machine Learning Engineer 4 to advance AI initiatives and scale production models across the organization. You will design and deploy ML models, build data pipelines, and collaborate with Product, Data Science, and Engineering teams in a fast-paced environment.

The role emphasizes responsible AI, model governance, and scalable cloud-based ML infra, using Python/Java/Golang/C++ and frameworks like PyTorch or TensorFlow.

Qualifications

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

Responsibilities

• Design, build, and deliver ML models and components in collaboration with Product and Data Science teams.
• Inform ML infrastructure choices, including model, data, features, training, and validation.
• Write, test, and deploy production-grade ML applications and data pipelines.
• Collaborate in a cross-functional Agile team to deliver ML-enabled software at scale.
• Retrain, monitor, and maintain models in production; ensure governance and responsible AI.
• Leverage cloud-based architectures to deliver scalable ML solutions and CSI/CD practices.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Spark

Ray

Kubernetes

AWS

GCP

Azure

Education

Bachelor's degree in Computer Science or related field

Tools

Spark

Ray

Kubernetes

AWS

GCP

Azure

Job description

Capital One is seeking a Machine Learning Engineer 4 to advance AI initiatives and scale production models across the organization. You will design and deploy ML models, build data pipelines, and collaborate with Product, Data Science, and Engineering teams in a fast-paced environment.

The role emphasizes responsible AI, model governance, and scalable cloud-based ML infra, using Python/Java/Golang/C++ and frameworks like PyTorch or TensorFlow.