Senior Director - Machine Learning Engineer Job in Capital One

What you ll do in the role: Lead an organization made up of machine learning, software, DevOps and data engineers to deliver on an ambitious agenda around building an industry leading machine learning platform that enables the training and serving of thousands of machine learning models. Leverage your experience building large scale systems to lead teams to scale our platform to support thousands of concurrent data scientists, research scientists and MLEs working concurrently to build models that scale. Leverage cloud-based architectures and technologies to deliver optimized enablement of ML models at scale. Optimize data pipelines to feed ML models. Use programming languages like Python, Scala, or Go. Promote best practices in all aspects of the engineering and modeling lifecycles. Recruit, nurture, and retain top engineering talent. Basic Qualifications Bachelor s Degree in STEM field (Science, Technology, Engineering, or Mathematics) with overall 15+ years of experience in Engineering At least 6 years of experience programming with Python, Scala, or Go. At least 5 years experience in Machine Learning, Software Engineering, or Data Engineering At least 5 years experience in technology leadership or managing people At least 5 years experience in a cloud environment Preferred Qualifications Master's or PhD degree in STEM field (Science, Technology, Engineering, or Mathematics) with overall 15+ years of experience in Engineering Experience building and supporting a multi-tenant machine learning platform providing model training and/or serving capabilities to users. 3+ years of experience building production-ready data pipelines that feed ML models. 5+ years experience in any major machine learning frameworks such as XGBoost, PyTorch, Tensorflow or AWS SageMaker 6+ years of experience within a large/data-intensive multi-line business environment. Expertise designing, implementing, and scaling complex production-ready data pipelines for ML models. Experience partnering with technology peers responsible for data architecture and distributed computing infrastructure/platforms Ability to communicate complex technical concepts clearly to a variety of audiences. Highly developed interpersonal, presentation, and communications skills. ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents.