Senior Lead Machine Learning Engineer (Intelligent Foundations and Experiences) | McLean, VA, USA

Senior Lead Machine Learning Engineer (Intelligent Foundations and Experiences) Senior Lead Machine Learning Engineer (Intelligent Foundations and Experiences) 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: • Lead dedicated pods of software, data and machine learning engineers in building AI/ML capabilities for Credit and Financial Risk Management products, serving as a technical mentor to the team on these core technologies • Design, build, and deliver AI-powered products and components that solve real-world business problems, leveraging expertise in model experimentation, LLM inference, similarity search, and agentic AI within a collaborative Product and Data Science environment • Collaborate with a cross-functional team of engineers, data scientists, and designers to develop and scale AI-powered products that enable optimized associate performance and deliver world-class customer value • 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 • 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 • Leverage a broad stack of Open Source and SaaS AI technologies and use programming languages like Python, Scala, or Java