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The Labs Incubator Intern position at Capital One for Summer 2025 is a paid internship that requires full-time commitment, amounting to 40 hours per week. Interns will be expected to participate in-person at their assigned location within the continental United States, adhering to Capital One's hybrid working model throughout the duration of the program. It is important to note that Capital One will not provide sponsorship for employment authorization or immigration-related support for this position, which includes various visa types such as H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, or any other work authorization. Candidates must be current students at the University of Maryland, College Park, or the University of Illinois, Urbana Champaign, and should be pursuing a Bachelor's degree or higher, with the expectation of obtaining their most recent degree by August 2026 or earlier. It is essential that interns continue in the same course of study following the completion of the internship. The role requires at least one year of experience with Python, and preferred qualifications include a degree in Computer Science, Computer Engineering, or a related field, along with experience in machine learning, relational databases, large-scale data analytics, and AWS. The Capital One Tech Incubator focuses on machine learning research, providing students with opportunities to contribute to innovative projects. Interns will engage in projects that primarily involve building cutting-edge machine learning platforms and products, utilizing modern software engineering practices such as large-scale data engineering, analytics, MLOps, CI/CD, and open source development. Capital One is committed to offering a comprehensive and competitive set of health, financial, and other benefits that support the total well-being of its employees. The company is an equal opportunity employer, dedicated to diversity and inclusion in the workplace, ensuring that all qualified applicants receive consideration for employment without discrimination based on various factors.
