Senior ML Engineer: Scale Production AI & Cloud Ops

Capital One; San Jose (CA); On-site; USD 251,000 - 286,000; Full time

Job summary

Capital One seeks a seasoned Machine Learning Engineer 5 to drive AI transformations and scale production models. You will design, build, and deploy ML models using PyTorch/TensorFlow, work with Spark/Ray for large-scale data, and operate in cloud ecosystems (AWS/GCP/Azure) with Kubernetes.

Expect collaboration with Product and Data Science teams in a fast-paced Agile environment. Required: 6+ years in ML with strong programming (Python/Java/C++/Golang), 6+ years in distributed systems, and 4+

Qualifications

• Bachelor's degree or higher in Computer Science, ML or related quantitative field.
• 6+ years Python/Java/Golang/C++ and 6+ years ML with PyTorch or TensorFlow.
• 6+ years deploying ML on large distributed systems (Spark, Ray).
• 4+ years deploying ML in production in cloud (AWS/GCP/Azure) with Kubernetes.

Responsibilities

• Design, build, and deliver ML models addressing real-world business problems with Product & Data Science.
• Scale multi-tenant platforms for training/serving ML at scale.
• Inform ML infra decisions on data, features, and model choices.
• Write, test, and deploy production ML code and automated tests.
• Collaborate in an Agile cross-functional team to build AI applications.
• Retrain, maintain, and monitor models in production.
• Leverage cloud architectures to deliver optimized ML models at scale.
• Build data pipelines feeding ML models and ensure CI/CD and monitoring.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Spark

Ray

Kubernetes

AWS

GCP

Azure

Education

Bachelor's degree in CS/ML or related

Master's/PhD desirable

Tools

Pandas

NumPy

Scikit-learn

Job description

Capital One seeks a seasoned Machine Learning Engineer 5 to drive AI transformations and scale production models. You will design, build, and deploy ML models using PyTorch/TensorFlow, work with Spark/Ray for large-scale data, and operate in cloud ecosystems (AWS/GCP/Azure) with Kubernetes.

Expect collaboration with Product and Data Science teams in a fast-paced Agile environment. Required: 6+ years in ML with strong programming (Python/Java/C++/Golang), 6+ years in distributed systems, and 4+