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
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
Our Benefits
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
Work Model
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 building.nubank.com/nu-hybrid-work-model/
Our recruitment process may involve the use of artificial intelligence–enabled 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.
To maintain a consistent and fair process for every candidate, Nu does not provide individualized technical feedback. See how our policy works here