Lead Machine Learning Engineer (Enterprise Platforms Technology)

Capital One seeks a Lead Machine Learning Engineer to advance production-ready ML applications and systems at scale within the Enterprise Platforms Technology group. The role centers on ML architectural design, hands-on development and review of model and application code, and ensuring high availability and performance across production environments. This onsite position is based in McLean, VA, with a compensation range of USD 197,300 to 225,100 per year.

Responsibilities

• Architect, build, and deliver ML models and components that address real-world business needs, in collaboration with Product and Data Science teams.
• Inform ML infrastructure decisions through knowledge of modeling techniques, including model selection, data and feature choices, training, hyperparameter tuning, dimensionality, bias/variance, and validation.
• Address complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
• Collaborate within a cross-functional Agile team to create and enhance software enabling state-of-the-art big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Apply continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Ensure code quality to reduce vulnerabilities, govern models from a risk perspective, and follow Responsible and Explainable AI practices.
• Utilize programming languages such as Python, Scala, or Java.

Requirements

• Bachelor’s Degree
• At least 6 years of experience designing and building data-intensive solutions using distributed computing (internship experience does not apply)
• At least 4 years of experience programming with Python, Scala, or Java
• At least 2 years of experience building, scaling, and optimizing ML systems

Technologies

• Python
• Scala
• Java
• scikit-learn
• PyTorch
• Dask
• Spark
• TensorFlow
• AWS
• Azure
• Google Cloud Platform

Benefits

• Performance-based incentive compensation (cash bonuses and/or long-term incentives)
• Health, financial and other benefits that support your total well-being