Machine Learning Engineering - Intelligent Foundations and Experiences (IFX) | Richmond, VA, USA

Machine Learning Engineering - Intelligent Foundations and Experiences (IFX) {"description": "Machine Learning Engineering - Intelligent Foundations and Experiences (IFX) As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.

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: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: • Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. • Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model 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 as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. • Retrain, maintain, and monitor models in production. • Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. • Construct optimized data pipelines to feed ML models. • Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. • Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. • Use programming languages like Python, Scala, or Java.