Lead ML Engineer: Scale AI Solutions (Python, AWS)

Socket.dev; New York (NY); On-site; USD 215,000 - 246,000; Full time

Job summary

Capital One is seeking a Lead Machine Learning Engineer in New York, NY to productionize ML applications at scale. You’ll collaborate with Product and Data Science teams to design, build, and deploy models using Python, Scala or Java, and leverage cloud architectures for performance.

You will architect ML systems, develop and review code, retrain and monitor models in production, and ensure governance and responsible AI practices while delivering scalable data pipelines.

Qualifications

• Bachelor’s degree required; advanced degree preferred.
• Minimum 6 years designing data-intensive solutions with distributed computing.
• At least 4 years programming in Python, Scala, or Java.
• At least 2 years building and optimizing ML systems.

Responsibilities

• Design and deliver ML models and components with Product/Data Science teams.
• Inform ML infra decisions and data/feature selection.
• Write, test, and deploy ML models and application code.
• Collaborate in an Agile cross-functional team for big data/ML apps.
• Retrain, monitor, and maintain models in production.
• Build cloud-based architectures to deliver scalable ML models.
• Create optimized data pipelines for ML models.
• Implement CI/CD, test automation, and monitoring for models and code.
• Ensure code governance and responsible/Explainable AI practices.

Skills

Python

Scala

Java

Distributed computing

Machine learning

Education

Bachelor's Degree

Master's or Doctoral Degree

Tools

scikit-learn

PyTorch

Spark

TensorFlow

Job description

Capital One is seeking a Lead Machine Learning Engineer in New York, NY to productionize ML applications at scale. You’ll collaborate with Product and Data Science teams to design, build, and deploy models using Python, Scala or Java, and leverage cloud architectures for performance.

You will architect ML systems, develop and review code, retrain and monitor models in production, and ensure governance and responsible AI practices while delivering scalable data pipelines.