Senior Manager - Machine Learning Engineer Job in Capital One

Responsibilities: Work with model and platform teams to build systems that ingest large amounts of model and feature metadata that will feed into automated governance decisioning. Partner with product and design teams to build elegant and scalable solutions to speed up governance processes. Collaborate as part of a cross-functional Agile team to create and enhance software that enables state of the art, next generation big data and machine learning applications. Leverage cloud-based architectures and technologies to deliver optimized ML models at scale. Work with your teams to build out data pipelines for ingesting and normalizing metadata across multiple disparate systems. Use programming languages like Python and Go. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployments of machine learning models and application code. Advocate for software and machine learning engineering best practices. Function as the engineering tech lead for large-scale initiatives. Recruit and mentor junior engineering talent. Basic Qualifications: Bachelor s degree At least 10 years of experience designing and building data intensive solutions using distributed computing. At least 4 years of experience programming with Python, Go, or Java. At least 3 years of experience building, scaling, and optimizing ML systems. At least 3 years of experience with the full ML Development Lifecycle using industry-recognized best practices. At least 2 years of experience leading teams developing ML solutions Preferred Qualifications: Master s Degree or PhD in Computer Science, Electrical Engineering, Mathematics, or a similar field. Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform. At least 4 years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow. At least 3 years of experience developing performant, resilient, and maintainable code. At least 3 years of experience with data gathering and preparation for ML models. Contributed to open source ML software. Authored/co-authored a paper on a ML technique, model, or proof of concept. Demonstrated ML industry impact through conference presentations, papers, blog posts, or patents. At least 3 years of experience building production-ready data pipelines that feed ML models. Ability to communicate complex technical concepts clearly to a variety of audiences.