Senior Lead Machine Learning Engineer

Capital One is seeking a Senior Lead Machine Learning Engineer to mature machine learning applications from concept to production at scale. In this capacity, you will shape the architecture, design, and deployment strategy within Agile teams, prioritizing reliability, governance, and performance. This on-site role is based in Richmond, Virginia, with a salary in the range of USD 209,000 to 238,500 per year.

Responsibilities

• Design and deliver ML models and components that address real world business problems, collaborating with Product and Data Science teams.
• Make informed ML infrastructure decisions by applying modeling techniques and addressing issues such as model choice, data, feature selection, training, hyperparameters, dimensionality, bias/variance, and validation.
• Tackle 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 build and enhance software powering advanced 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, governance, risk management, and adherence to Responsible and Explainable AI practices.
• Demonstrate proficiency in Python, Scala, or Java.

Requirements

• Bachelor's Degree
• At least 8 years designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
• At least 4 years programming with Python, Scala, or Java
• At least 3 years building, scaling, and optimizing ML systems
• At least 2 years leading teams developing ML solutions

Technologies

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

Benefits

• Health benefits
• Financial benefits
• Performance-based incentives (cash bonuses and long-term incentives)

Preferred Qualifications

• Master's or doctoral degree in computer science, electrical engineering, mathematics, or a related field
• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
• 4+ years of experience with industry-recognized ML frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
• 3+ years of experience developing performant, resilient, and maintainable code
• 3+ years of experience with data gathering and preparation for ML models
• 3+ years of people management experience
• ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
• 3+ years building production-ready data pipelines that feed ML models
• Ability to communicate complex technical concepts clearly to a variety of audiences