The posting, in Nubank's own words
archived Oct 20, 2026About Nu
Nu serves more than 140 million customers, guided by a mission to fight complexity and empower people. The company has been leading an industry transformation through innovative products and human-centered services. Proprietary technology and data at scale power Nu’s digital platform, built to promote financial access, advancement, and transparency. Its business model thrives on customer love and lower costs, feeding a flywheel of growth and profitability. Visit our Institutional Page
About the Program
The Nu AI Core Summer Internship Program is a research-focused internship for PhD students working at the frontier of artificial intelligence and machine learning. Over about 12 weeks, interns collaborate with engineers, applied scientists, and researchers on complex AI challenges. Each intern leads an end-to-end research or applied AI project that combines research methods with large-scale production environments. The program builds long-term relationships between industry and academia and with emerging AI research talent. Program Structure Duration: ~12 weeks Start dates: May–June, aligned with US university calendars Location model : Remote, with one in-person week at Nu's Palo Alto office in Silicon Valley, fully covered by us. What you get Dedicated research mentor Ownership of a scoped AI research project Access to large-scale datasets and compute infrastructure Research talks and technical seminars Final research presentation to leadership and AI teams
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Interns may work on projects related to:
Machine learning and deep learning Large language models and generative AI Recommender systems and ranking models AI infrastructure and model optimization Responsible AI and model evaluation Projects typically combine research experimentation with applied system development , including model design, experimentation, and large-scale evaluation. Candidate Profile This program targets PhD students conducting research in AI and who are interested in applying their work to real-world systems.