Lead Machine Learning Engineer

Capital One is seeking a Lead Machine Learning Engineer to join an onsite team in McLean, Virginia. The role focuses on productionizing ML applications and systems at scale, architecting solutions, developing and reviewing code, and maintaining high availability and performance within an Agile environment. The position offers an annual salary range of USD 197,300 to 225,100.

Responsibilities

• Design, build, and deliver ML models and components that address real world business problems, in collaboration with Product and Data Science teams
• Inform ML infrastructure choices using knowledge of modeling techniques, including model selection, data and feature choices, training, hyperparameter tuning, dimensionality, bias/variance, and validation
• Solve 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 for state of the art big data and ML applications
• Retrain, maintain, and monitor models in production
• Utilize 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 and governance, manage risk, and align ML practices with responsible and explainable AI standards
• Work with 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, which may include cash bonus(es) and/or long term incentives (LTI)
• Health benefits
• Financial benefits
• Other benefits