Senior ML Engineer | Scalable AI Platforms

Capital One; McLean (VA); On-site; USD 230,000 - 262,000; Full time

Job summary

Capital One is seeking a senior-level Machine Learning Engineer to design, build and scale ML models and platforms in a production environment. You will collaborate across product, data science, and engineering teams to deliver cutting-edge AI solutions at scale.

You will own the end-to-end ML lifecycle, including model training, deployment, monitoring, and governance, with emphasis on responsible AI and explainability in a cloud-native stack.

Qualifications

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

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams
• Build and scale multi-tenant platforms for running large footprint ML training and serving at scale
• Inform ML infrastructure decisions: data, features, model choice, and validation
• Write and test application code, develop and validate ML models, and automate tests and deployment
• Collaborate in cross-functional Agile team to create software for big data and ML
• Retrain, maintain, and monitor models in production; maintain governance and explainability

Skills

Python

Java

Golang

C++

ML modeling

Distributed systems

Education

Bachelor's degree in CS/ML or related field

Master's or PhD preferred

Tools

PyTorch

TensorFlow

Spark

Ray

Kubernetes

AWS/GCP/Azure

Job description

Capital One is seeking a senior-level Machine Learning Engineer to design, build and scale ML models and platforms in a production environment. You will collaborate across product, data science, and engineering teams to deliver cutting-edge AI solutions at scale.

You will own the end-to-end ML lifecycle, including model training, deployment, monitoring, and governance, with emphasis on responsible AI and explainability in a cloud-native stack.