Senior Machine Learning Engineer - Scalable ML & GenAI

Capital One; McLean (VA); On-site; USD 197,000 - 225,000; Full time

Job summary

Capital One seeks a Lead Machine Learning Engineer to join the Enterprise Platforms Technology group in McLean, VA. You will design, build, and productionize ML applications at scale, collaborating with Product and Data Science teams to deliver high-impact solutions.

The role focuses on developing ML models, building data pipelines, and implementing CI/CD practices while ensuring governance and responsible AI. Proficiency in Python, cloud platforms, and distributed systems is essential.

Qualifications

• Bachelor’s Degree is required.
• At least 6 years designing and building data-intensive solutions using distributed computing.
• At least 4 years programming with Python, Scala, or Java.
• At least 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 including data, features, training, and validation.
• Write, test, and deploy application code; automate tests and deployment.
• Collaborate in a cross-functional Agile team to enable state-of-the-art ML applications.
• Retrain, monitor, and maintain models in production.
• Leverage cloud architectures to deliver scalable ML models.
• Construct optimized data pipelines for ML models.
• Follow CI/CD and monitoring best practices; ensure governance and responsible AI.

Skills

Python

AWS

SQL

GenAI

Java

Scala

ML Engineering

Education

Bachelor’s Degree

Master’s or Doctoral Degree in CS/EE/Math

Tools

scikit-learn

PyTorch

Dask

Spark

TensorFlow

Job description

Capital One seeks a Lead Machine Learning Engineer to join the Enterprise Platforms Technology group in McLean, VA. You will design, build, and productionize ML applications at scale, collaborating with Product and Data Science teams to deliver high-impact solutions.

The role focuses on developing ML models, building data pipelines, and implementing CI/CD practices while ensuring governance and responsible AI. Proficiency in Python, cloud platforms, and distributed systems is essential.