AI Native Staff Software Engineer - Insurance

Nubank·Canada, Toronto; USA, Miami; USA, Palo Alto·onsite
crypto:applicationengineeringIC6Engineer
Compensation
Not disclosed
AI Native Staff Software Engineer About Us Nu is one of the largest digital financial platforms in the world, serving more than 135 million customers across Brazil, Mexico, and Colombia. Guided by our mission to fight complexity and empower people, we are redefining financial services in Latin America and beyond — and this is still just the beginning of the purple future we’re building. We combine proprietary technology, data intelligence, and an efficient operating model to deliver financial products that are simple, accessible, and human. Nu IPO’d in 2021 on the New York Stock Exchange (NYSE: NU), after being founded in 2013 and backed by leading global investors. Learn more at: https://international.nubank.com.br/careers/ About the role At Nubank, AI is not a bolt-on feature — it is existential to our next phase of growth. We are becoming an AI-native company , where intelligent systems don’t just support our products; they define them. As an AI Native Staff Software Engineer – Insurance , you will drive this transformation in our Insurance BU, which sits at the intersection of embedded insurance, Nu’s most important customer surfaces, and our insurer partners. You will be part of a lean, entrepreneurial team that delivers outsized business impact while protecting millions of customers, and is an early adopter of agentic engineering within Nubank. This role combines high-impact technical leadership with hands-on building: you will design and operate production AI/agentic systems, set engineering standards, and help define what it means for Nubank to be truly AI-native. You’ll be responsible for Designing, building and managing agentic workflows that power end-to-end delivery of customer-facing insurance features and journeys. Setting and evolving standards for AI development , promoting reusable agents, commands and context structures that other teams can safely build on. Establishing evaluation and monitoring practices for AI outputs, ensuring qu