Lead ML Engineer – Scalable GenAI & Cloud AI Systems

Capital One National Association; New York (NY); On-site; USD 215,000 - 246,000; Full time

Job summary

Capital One is seeking a Lead Machine Learning Engineer to productionize ML applications at scale within the Enterprise Platforms Technology group in New York, NY. This role focuses on ML architectural design, model and application code development, and ensuring high availability and performance of ML systems.

You'll collaborate across Product, Data Science, and cross-functional teams to deliver scalable ML pipelines, retrain and monitor models in production, and leverage cloud-based

Qualifications

• Bachelor’s Degree in a relevant field.
• 6+ years designing and building data-intensive solutions using distributed computing.
• 4+ years programming with Python, Scala, or Java.
• 2+ years building, scaling, and optimizing ML systems.

Responsibilities

• Design, build, and deliver ML models and components in collaboration with Product and Data Science teams.
• Inform ML infrastructure decisions using ML modeling techniques, data, features, training, and validation.
• Write and test application code, validate models, and automate tests and deployment.
• Collaborate in a cross-functional Agile team to create software for ML at scale.
• Retrain, maintain, and monitor models in production.
• Leverage cloud architectures to deliver ML models at scale.
• Construct optimized data pipelines for ML models.
• Apply CI/CD, test automation, and monitoring for ML deployments.
• Ensure code governance and Responsible AI practices.
• Use Python, Scala, or Java.

Skills

Distributed computing

Python

Scala

Java

ML model deployment

Cross-functional collaboration

Education

Bachelor's Degree

Master's or Doctoral Degree

Tools

PyTorch

scikit-learn

Spark

TensorFlow

Dask

AWS

Azure

GCP

Job description

Capital One is seeking a Lead Machine Learning Engineer to productionize ML applications at scale within the Enterprise Platforms Technology group in New York, NY. This role focuses on ML architectural design, model and application code development, and ensuring high availability and performance of ML systems.

You'll collaborate across Product, Data Science, and cross-functional teams to deliver scalable ML pipelines, retrain and monitor models in production, and leverage cloud-based