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Staff Machine Learning Engineer

Nubank
CompanyNubank
CategoryEngineering
LocationPalo Alto
RemoteHybrid
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
Posted20 Jul 2026
Last verified30 Jul 2026
SourceEmployer career page (ashby)
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Description
ABOUT NU Nu is the leading digital bank in Latin America, serving 135 million customers across Brazil, Mexico, and Colombia. The company has been leading an industry transformation by leveraging data and proprietary technology to develop innovative products and services. Guided by its mission to fight complexity and empower people, Nu caters to customers’ complete financial journey, promoting financial access and advancement with responsible lending and transparency. The company is powered by an efficient and scalable business model that combines low cost to serve with growing returns. Nu’s impact has been recognized in multiple awards, including Time 100 Most Influential Companies, Fast Company’s Most Innovative Companies, and Forbes World’s Best Banks. Visit our Institutional Page https://www.nu.com/2026-en ABOUT THE ROLE At AI Core, we are scaling the impact of our AI initiatives to become the primary driver of Nubank’s most critical decision systems. We are seeking  Machine Learning Engineers to lead high-impact research projects that bridge the gap between state-of-the-art AI and production-grade financial systems. You will be responsible for solving complex, ambiguous problems using Deep Learning and Foundation Models, ensuring our architectures are scalable, efficient, and driving measurable business results. As an Machine Learning Engineer (MLE), you’re expected to: 1. Research Execution & Technical Leadership (Complexity & Autonomy) - Lead and execute complex applied research initiatives independently, focusing on building and optimizing architectures (e.g., Transformers, GNNs) that can be deployed across critical use cases like Credit, RecSys, GenAI, and real-time inference. - Address difficult and ambiguous modeling problems that require coordination across various stakeholders (Data, Infra, Product), delivering innovative solutions with a clear focus on medium-term impact. - Bridge the gap between research and production by designing architectures that respect MLOps constraints, ensuring models are optimized for latency, interpretability, and cost-efficiency. 1. Strategic Impact & Collaboration (Impact) - Develop and deliver innovative solutions that address project-level challenges, focusing on pushing the latest platform and AI research improvements into downstream production models. - Actively participate in cross-functional collaborations, ensuring that research outputs are seamlessly integrated into Nubank's decision-making engines. - Establish technical standards within the AI Core team for experimentation, model evaluation, and code quality, inspiring peers to raise their performance. 1. Mentorship & Function Contribution (Function Contribution) - Serve as a technical mentor for senior engineers and researchers, providing guidance on deep learning fundamentals, problem formulation, and research methodology. - Actively contribute to the function's growth by participating in mandatory activities like hiring (interview panels) and leading internal task forces to improve our ML lifecycle. - Contribute to thought leadership by participating in research collaborations or internal papers that align with Nubank’s strategic goals. What are we looking for? - Professional Experience: 5-7+ years in applied AI/ML, with a proven track record of delivering research-driven systems into production environments - Technical Mastery: - Deep expertise in Deep Learning architectures (Transformers, Multimodal, or GNNs). - Strong coding skills in Python and proficiency with frameworks like PyTorch, JAX, or TensorFlow. - Solid understanding of MLOps and the constraints of deploying models at scale. - Problem Solving: Sophisticated skills in ML problem formulation and the ability to navigate uncertainty when data is messy or unavailable. - Communication: Ability to communicate complex technical concepts to both technical peers and cross-functional stakehol
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