Senior Machine Learning Engineer - Production & Scale

Capital One; United States; On-site; USD 230,000 - 262,000; Full time

Job summary

Capital One is seeking a Machine Learning Engineer 5 to design and deploy production ML models at scale. You will collaborate with Product and Data Science teams to build end-to-end ML solutions and optimize data pipelines for training and inference.

Lead in the design of multi-tenant ML platforms, monitor production models, and apply responsible AI practices while leveraging Python, Java, or Scala in a cloud environment with Kubernetes-based deployments.

Qualifications

• Bachelor’s degree in Computer Science, Machine Learning or related quantitative field.
• At least 6 years of experience programming with Python, Java, Golang, or C++.
• At least 6 years of ML experience using PyTorch or TensorFlow and libraries Pandas, NumPy, Scikit-learn.
• At least 6 years of experience using and operating large scale distributed systems (Spark, Ray).
• At least 4 years deploying and operating ML solutions in production in the cloud (AWS, GCP, Azure) and using Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components solving real-world business problems.
• Build and scale multi-tenant platforms enabling large footprint ML training and serving at scale.
• Inform ML infrastructure decisions including model/data/feature selection, training, tuning, and validation.
• Retrain, maintain, and monitor models in production and on cloud infrastructure.

Skills

Python

Java

Golang

C++

Education

Bachelor’s Degree in CS or related field

Master’s or PhD in CS/Math/EE

Tools

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Kubernetes

AWS

GCP

Azure

Job description

Capital One is seeking a Machine Learning Engineer 5 to design and deploy production ML models at scale. You will collaborate with Product and Data Science teams to build end-to-end ML solutions and optimize data pipelines for training and inference.

Lead in the design of multi-tenant ML platforms, monitor production models, and apply responsible AI practices while leveraging Python, Java, or Scala in a cloud environment with Kubernetes-based deployments.