About 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.

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Machine Learning Engineer at Nubank

At Nubank, Machine Learning Engineers sit at the core of how we make decisions at scale. We build, train, and deploy models that drive credit, fraud, risk, personalization decisions and a growing set of AI-native experiences for millions of customers every day. We do it with engineering rigor, statistical depth, and a deep focus on impact.

Our MLEs work across the full modeling lifecycle: framing business problems as ML problems, engineering features, training and validating models, and deploying and monitoring them in production. We value small, independent teams that move fast, own their decisions end-to-end, and hold themselves to a high bar for quality and craft.

Increasingly, that work also includes Generative AI and Agentic Engineering. Depending on the problem, our engineers design and build systems that combine models, tools, workflows, evaluation loops, and human oversight to solve real business tasks reliably in production.

We strive for state-of-the-art ML practices that currently include a variety of technologies. While we value candidates that are familiar with them, we are also confident that engineers who are interested in joining Nubank will be able to learn from our team.

Large-scale model training and experimentation pipelines

Feature engineering and feature stores feeding both batch and real-time models

Model deployment and serving in production, with monitoring through operational and business metrics

Distributed data processing for training datasets at scale

Continuous Integration and Deployment into AWS and Kubernetes

Experiment tracking, model versioning, and reproducibility tooling

A robust data platform built on modern ETL/ELT practices

As a Machine Learning Engineer, you’re expected to

Frame ambiguous business problems as well-defined modeling problems

Design, build and validate machine learning models, ensuring statistical rigor and business relevance

Engineer and maintain features and datasets used for training and inference

Deploy and maintain ML models in both batch and real-time scenarios, integrating them with other systems and monitoring through operational and business metrics

Lead modeling projects end-to-end — from problem framing and stakeholder alignment to delivery, monitoring and iteration

Contribute to the design, documentation, maintenance and optimization of our modeling codebase, platforms and tooling

Translate business needs into modeling strategies aligned with Nubank's architecture and long-term goals

Partner with technical and business stakeholders to define strategies and deliver high-impact models

Share knowledge, mentor peers and contribute to ML and data literacy initiatives across Nubank

What We're Looking For

Strong foundation in statistics, machine learning theory and modeling techniques (e.g. regression, tree-based models, deep learning)

Programming experience in Python and familiarity with ML libraries (e.g. scikit-learn, PyTorch, TensorFlow, XGBoost)

Experience training, validating, and tuning models, with solid understanding of overfitting, bias-variance tradeoff and evaluation metrics

Understanding of the ML model lifecycle, from training and evaluation to deployment and monitoring

Ability to write efficient SQL queries and work with analytical data environments

Strong communication skills to collaborate with both technical and business stakeholders

Passion for building high-quality, production-grade models

Nice to Have

Experience with cloud platforms such as AWS, GCP or Azure

Familiarity with distributed systems, microservices and asynchronous architectures