Senior ML Engineer & Tech Lead

Capital One National Association; Chicago (IL); On-site; USD 179,000 - 205,000; Full time

Job summary

Capital One in Chicago is seeking a Machine Learning Engineer who blends ML, software engineering, and data engineering. You will design, build, and deploying ML models and components that solve real-world business problems, collaborating with Product and Data Science teams.

You will train, retrain, monitor models in production, optimize data pipelines, and leverage cloud platforms (AWS, GCP, Azure) and Kubernetes. Strong Python/Java/Golang/C++ and PyTorch/TensorFlow experience are required.

Qualifications

• Bachelor's Degree or higher in CS, ML or related quantitative field.
• At least 4 years programming with Python, Java, Golang, or C++.
• At least 4 years ML experience using PyTorch or TensorFlow and libraries (Pandas, NumPy, Scikit-learn).
• At least 4 years experience with large-scale distributed systems (Spark, Ray).
• At least 2 years deploying 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 problems with Product and Data Science teams.
• Inform ML infrastructure decisions by model choice, data, feature selection, and validation.
• Write and test production code, train/validate models, and automate tests and deployment.
• Collaborate in a cross-functional Agile team to enable ML apps and big data systems.
• Retrain, monitor, and maintain models in production; optimize data pipelines.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

Kubernetes

AWS

GCP

Azure

Education

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

Master's or Doctoral Degree in CS / EE / Math / related field

Tools

Spark

Ray

Kubernetes

Job description

Capital One in Chicago is seeking a Machine Learning Engineer who blends ML, software engineering, and data engineering. You will design, build, and deploying ML models and components that solve real-world business problems, collaborating with Product and Data Science teams.

You will train, retrain, monitor models in production, optimize data pipelines, and leverage cloud platforms (AWS, GCP, Azure) and Kubernetes. Strong Python/Java/Golang/C++ and PyTorch/TensorFlow experience are required.