Senior ML Engineer — Enterprise Platforms (Production-Scale)

Energy Jobline ZR; McLean (VA); On-site; USD 197,000 - 225,000; Full time

Job summary

Capital One is seeking a Lead Machine Learning Engineer (MLE) for Enterprise Platforms Technology in McLean, VA. You will design, deploy and scale ML models, collaborate across Ops, Modeling and Data Engineering, and ensure production-grade, explainable AI practices.

You’ll work in an Agile team using Python/Scala/Java, building pipelines and cloud-based ML architectures to deliver state-of-the-art solutions with high availability and performance.

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.
• 2+ years leading teams developing ML solutions.
• Experience with public cloud (AWS, Azure, or GCP).

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform ML infrastructure decisions including model choice, data, and feature selection.
• Write and test application code, develop and validate ML models, and automate tests and deployment.
• Collaborate in a cross-functional Agile team to enable state-of-the-art ML apps.
• Retrain, maintain, and monitor models in production.
• Leverage cloud architectures to deploy optimized ML models at scale.
• Build data pipelines feeding ML models and ensure governance.

Skills

Python

Scala

Java

Distributed computing

ML systems

Education

Master's or Doctoral Degree

Tools

scikit-learn

PyTorch

TensorFlow

Spark

Dask

AWS

Azure

GCP

Job description

Capital One is seeking a Lead Machine Learning Engineer (MLE) for Enterprise Platforms Technology in McLean, VA. You will design, deploy and scale ML models, collaborate across Ops, Modeling and Data Engineering, and ensure production-grade, explainable AI practices.

You’ll work in an Agile team using Python/Scala/Java, building pipelines and cloud-based ML architectures to deliver state-of-the-art solutions with high availability and performance.