Senior Lead ML Engineer - Build Production-Grade AI

Capital One; Chicago (IL); On-site; USD 209,000 - 239,000; Full time

Job summary

Capital One seeks an experienced Machine Learning Engineer to join an Agile team focused on productionizing ML applications at scale. You will design, implement, and optimize ML models and pipelines, collaborating with Product and Data Science to deliver scalable, governed solutions.

Strong emphasis on production readiness, monitoring, and responsible AI. You will lead ML initiatives with cloud-based architectures, CI/CD practices, and cross-functional collaboration, while continuously learning

Qualifications

• Bachelor’s Degree; 8+ years designing data-intensive solutions using distributed computing.
• At least 4 years programming in Python/Scala/Java and 3+ years building ML systems.
• 3+ years leading teams developing ML solutions and building production-ready data pipelines.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform ML infrastructure decisions: model choice, data, feature selection, training, and validation.
• Write and test application code, automate tests and deployment for ML models and apps.
• Collaborate in an Agile team to enable state-of-the-art big data and ML applications.
• Retrain, maintain, and monitor models in production; ensure governance and responsible AI practices.

Skills

Distributed computing

Programming: Python/Scala/Java

ML engineering

CI/CD

Cloud platforms (AWS/Azure/GCP)

Team leadership

Communication

Education

Bachelor’s Degree

Master’s or Doctoral in CS/EE/Math (preferred)

Tools

scikit-learn

PyTorch

Dask

Spark

TensorFlow

Job description

Capital One seeks an experienced Machine Learning Engineer to join an Agile team focused on productionizing ML applications at scale. You will design, implement, and optimize ML models and pipelines, collaborating with Product and Data Science to deliver scalable, governed solutions.

Strong emphasis on production readiness, monitoring, and responsible AI. You will lead ML initiatives with cloud-based architectures, CI/CD practices, and cross-functional collaboration, while continuously learning