Senior Lead Machine Learning Engineer

Capital One seeks a Senior Lead Machine Learning Engineer to operationalize machine learning applications at scale within an Agile environment, focusing on ML architecture and end-to-end development and deployment, with an emphasis on responsible and explainable AI.

Compensation

Salary range: USD 229,900 to 262,400 per year.

Responsibilities

• Design and deliver ML models and components that address real business needs, collaborating with Product and Data Science teams.
• Guide ML infrastructure decisions based on modeling techniques, including model selection, data and feature choices, training, hyperparameter tuning, dimensionality considerations, bias and variance, and validation.
• Tackle complex problems by developing and testing application code, building and validating ML models, and automating tests and deployment.
• Join a cross-functional Agile team to create software enabling cutting edge big data and ML applications.
• Retrain, monitor, and maintain models in production environments.
• Leverage or construct cloud based architectures, technologies, and platforms to deliver optimized ML models at scale.
• Build optimized data pipelines to feed ML models.
• Apply continuous integration and continuous deployment practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Ensure code is well governed to minimize vulnerabilities and that models comply with risk considerations and Responsible and Explainable AI practices.
• Proficient in programming languages such as Python, Scala, or Java.

Requirements

• Bachelor’s Degree.
• Minimum eight years designing and delivering data-intensive solutions using distributed computing, with internships not counted.
• At least four years programming with Python, Scala, or Java.
• At least three years building, scaling, and optimizing ML systems.
• At least two years leading teams developing ML solutions.

Technologies

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

Benefits

• Performance-based incentive eligibility
• Health, financial and other benefits that support total well being

Basic Qualifications

• Bachelor’s Degree
• Minimum eight years designing and delivering data-intensive solutions using distributed computing (internships excluded)
• At least four years programming with Python, Scala, or Java
• At least three years building, scaling, and optimizing ML systems
• At least two years leading teams developing ML solutions

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 on-the-job experience with industry recognized ML frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
• 3+ years building performant, resilient, and maintainable code
• 3+ years of experience gathering and preparing data for ML models
• 3+ years of people management experience
• Impact in the ML field through conferences, 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 diverse audiences