Senior ML Engineer: Build & Scale Production Models

Capital One; McLean (VA); On-site; USD 162,000 - 185,000; 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 will design, build, and deploy ML models and contribute to architecture, data pipelines, and scalable deployment.

You will collaborate with Product and Data Science teams, apply modern ML frameworks, and implement CI/CD with model monitoring. The role emphasizes governance, responsible AI, and cloud-based ML delivery.

Qualifications

• Bachelor’s degree required in a related field.
• 4+ years programming with Python, Scala, or Java.
• 3+ years building data-intensive distributed solutions.
• 2+ years with ML frameworks (scikit-learn, PyTorch, Spark, or TensorFlow).
• 1+ year productionizing, monitoring, and maintaining models.

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, training, tuning, and validation.
• Develop and test application code, validate ML models, and automate tests and deployment.
• Collaborate in an Agile team to enable state-of-the-art big data and ML applications.
• Retrain, monitor, and maintain models in production.
• Leverage cloud architectures and platforms for scalable ML delivery.
• Construct optimized data pipelines feeding ML models and ensure CI/CD with monitoring.
• Ensure secure, governed ML practices and responsible/Explainable AI.

Skills

Python/Scala/Java

Distributed computing

ML frameworks

Productionizing ML models

Education

Bachelor's Degree

Tools

Cloud platforms (AWS/Azure/GCP)

Job description

Capital One is seeking a Senior Machine Learning Engineer to join an Agile team focused on productionizing ML applications at scale. You will design, build, and deploy ML models and contribute to architecture, data pipelines, and scalable deployment.

You will collaborate with Product and Data Science teams, apply modern ML frameworks, and implement CI/CD with model monitoring. The role emphasizes governance, responsible AI, and cloud-based ML delivery.