Lead Machine Learning Engineer / Applied AI Scientist

Nu was born in 2013 with the mission to fight complexity to empower people in their daily lives by reinventing financial services. We are one of the world’s largest digital banking platforms, serving millions of customers across Brazil, Mexico, and Colombia.

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 an Lead Machine Learning Engineer (Applied AI Scientist) 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 Applied AI Scientist (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.

• 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 stakeholders, ensuring alignment and buy-in.
• Analytical Capacity: Experience with large-scale experimentation and A/B testing to validate research hypotheses.

Hybrid 2-3 times/week: Our hybrid work model brings us to the office at least twice a week, on strategic days designed to maximize team connection and collaboration. For more details, visit https://building.nubank.com/nu-hybrid-work-model/