Lead Machine Learning Engineer

Nu is the leading digital bank in Latin America serving 140 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.

Nus impact has been recognized in multiple awards including Time 100 Most Influential Companies Fast Companys Most Innovative Companies and Forbes Worlds Best Banks.

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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

• 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 Nubanks 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

• 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

• Experience with cloud platforms such as AWS GCP or Azure

• Familiarity with distributed systems microservices and asynchronous architectures

• Experience with feature stores MLOps tooling and experiment tracking (e.g. MLflow Feast Airflow)

• Knowledge of data architecture patterns (Data Lake Data Warehouse Data Mart)

• Experience with data visualization tools (Looker Power BI Tableau or similar)

Knowledge of software engineering best practices: testing clean code documentation

• Chance of earning equity at Nubank

• Food/Meal Card (Vale-Refeição and/or Vale Alimentação)

• Public Transportation Commuting Benefit (Vale-Transporte)

• NuCare Psychological Financial and Legal Assistance Program

• Life Insurance Medical Plan and Dental Plan

• NuLanguage Language Course Program

• Nucleo Our learning platform

• Extended Parental Leave Daycare Allowance and Parental Consultancy

• Work-from-home Allowance

• Gym Partnerships

• 30 days of paid vacation

• Relocation Assistance Package if applicable

Hybrid 23 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 recruitment process may involve the use of artificial intelligenceenabled tools such as automated interview transcription and analysis to support the evaluation process. Artificial intelligence is not used to make final hiring decisions; all decisions are made by human reviewers.

Required Experience:

IC