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Partner with a cross-functional team to deliver AI-powered products, build AI foundation models, engage in applied research, and translate technical work into business goals.

Lead the data transformation efforts to standardize and modernize data feeds for a finance organization. Define and operationalize important data, work with cross-functional teams to create roadmaps and deliver scalable data products. Prioritize discovery, define MVPs, and lead data modernization sequencing.

As a Distinguished Applied Researcher at Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses. You will partner with cross-functional teams to deliver AI-powered products, leverage technologies to analyze large volumes of data, build AI models, and engage in high impact applied research.

This position is for a Sr. Distinguished Applied Researcher at Capital One. The role involves partnering with cross-functional teams to deliver AI-powered products and build AI foundation models. The ideal candidate is innovative, creative, a leader, and technically skilled.

Partner with a cross-functional team to deliver AI-powered products, build AI foundation models, engage in applied research, and translate complex work into tangible business goals.

As a Senior Lead Machine Learning Engineer at Capital One, you will design, build, and deliver ML models to solve real-world business problems. You will lead teams developing ML solutions, optimize ML systems, and ensure high availability and performance of machine learning applications. Additionally, you will leverage cloud-based architectures and continuous deployment best practices to deliver optimized ML models at scale.

As a Senior Lead Machine Learning Engineer at Capital One, you will be responsible for designing, building, and delivering machine learning models and components to solve real-world business problems. You will collaborate with cross-functional teams to enhance software for big data and ML applications, retrain and maintain models in production, and ensure high availability and performance of machine learning applications.