Senior ML Engineer — Build & Scale Production AI

Capital One; Richmond (VA); On-site; USD 147,000 - 168,000; Full time

Job summary

Capital One is seeking a Senior Machine Learning Engineer to join an Agile team focused on productionizing ML applications at scale. You’ll design and implement ML models, build data pipelines, and ensure high availability of systems using Python, Scala, or Java.

The role emphasizes collaboration with Product and Data Science teams, model governance, and cloud-based architectures. Strong ML framework experience and distributed data skills are needed for success.

Qualifications

• Bachelor’s degree required; advanced degree preferred.
• 4+ years programming in Python, Scala, or Java.
• 3+ years with distributed computing and data-intensive solutions.
• 2+ years productionizing, monitoring, and maintaining ML models.
• Experience with ML frameworks (scikit-learn, PyTorch, Spark, TensorFlow).

Responsibilities

• Design, build, and deliver ML models and components with cross-functional teams.
• Inform ML infra decisions including model choice, data and features.
• Develop and test application code for ML models and automate tests/deployments.
• Collaborate in an Agile team to enable big data and ML apps.
• Retrain, monitor, and maintain models in production.
• Leverage cloud architectures to deliver ML models at scale.
• Construct optimized data pipelines for ML models.
• Follow CI/CD and monitoring to ensure reliable deployments.
• Ensure code governance and Responsible AI practices.
• Use Python, Scala, or Java in development.

Skills

Python

Scala

Java

Distributed computing

ML frameworks

Model productionizing

Data pipelines

Cloud platforms

Open source ML

Research papers (ML)

Education

Bachelor's degree

Master's or doctoral degree (preferred)

Tools

AWS

Azure

Google Cloud Platform

Spark

TensorFlow

Job description

Capital One is seeking a Senior Machine Learning Engineer to join an Agile team focused on productionizing ML applications at scale. You’ll design and implement ML models, build data pipelines, and ensure high availability of systems using Python, Scala, or Java.

The role emphasizes collaboration with Product and Data Science teams, model governance, and cloud-based architectures. Strong ML framework experience and distributed data skills are needed for success.