Senior ML Engineer, MLX: Production AI & Scale

Capital One; San Francisco (CA); On-site; USD 215,000 - 246,000; Full time

Job summary

Capital One is seeking a Machine Learning Engineer 4 to advance AI initiatives across scalable ML models and data pipelines. You will collaborate with product and data science teams to deliver production-grade solutions using PyTorch/TensorFlow, Spark, and Kubernetes in cloud environments.

The role emphasizes building robust ML systems, practicing responsible AI, and following CI/CD workflows while communicating insights to cross-functional teams in a fast-paced setting.

Qualifications

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

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform ML infrastructure decisions including data, features, training, and validation.
• Write production-grade code, validate models, and automate tests and deployment.
• Collaborate in an Agile cross-functional team to enable scalable ML apps.
• Retrain, monitor, and maintain models in production.
• Leverage cloud architectures to deliver ML models at scale.
• Create optimized data pipelines to feed ML models.
• Ensure code quality and governance and responsible AI practices.
• Use Python, Scala, or Java for implementations.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

Kubernetes

Education

Bachelor's degree in CS or related quantitative field

Master's or Doctoral degree (preferred)

Tools

AWS

GCP

Azure

CI/CD

Job description

Capital One is seeking a Machine Learning Engineer 4 to advance AI initiatives across scalable ML models and data pipelines. You will collaborate with product and data science teams to deliver production-grade solutions using PyTorch/TensorFlow, Spark, and Kubernetes in cloud environments.

The role emphasizes building robust ML systems, practicing responsible AI, and following CI/CD workflows while communicating insights to cross-functional teams in a fast-paced setting.