Senior ML Engineer: Production-Scale AI Lead

Capital One Group; McLean (VA); Hybrid; USD 230,000 - 262,000; Full time

Job summary

Capital One seeks a Sr Lead Machine Learning Engineer to join an Agile team focused on productionizing ML applications at scale. You will design, build and review models, select features, and ensure high availability of ML systems. You will guide ML infrastructure choices and collaborate with Product and Data Science across the stack.

The role emphasizes scalable model deployment, performance optimization, and governance of AI practices in a fast-paced environment.

Qualifications

• Bachelor’s degree required.
• 8+ years designing and building data-intensive solutions using distributed computing.
• 4+ years programming with Python, Scala or Java.
• 3+ years building, scaling, and optimizing ML systems.
• 2+ years leading teams developing ML solutions.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform ML infrastructure choices: model, data, features, training, tuning, validation.
• Write and test application code, validate ML models, and automate tests/deployment.
• Collaborate in an Agile cross-functional team to enable big data and ML apps.
• Retrain, monitor, and maintain models in production.
• Leverage cloud architectures to deliver optimized ML models at scale.

Skills

Python

Scala

Java

Distributed computing

ML systems

Team leadership

Cloud platforms

Education

Bachelor's degree

Master's or PhD ( Preferred )

Tools

AWS

Azure

GCP

scikit-learn

PyTorch

TensorFlow

Spark

Dask

Job description

Capital One seeks a Sr Lead Machine Learning Engineer to join an Agile team focused on productionizing ML applications at scale. You will design, build and review models, select features, and ensure high availability of ML systems. You will guide ML infrastructure choices and collaborate with Product and Data Science across the stack.

The role emphasizes scalable model deployment, performance optimization, and governance of AI practices in a fast-paced environment.