机器学习工程师负责人
Lead Machine Learning Engineer
Nu 是拉丁美洲领先的数字银行，为巴西、墨西哥和哥伦比亚的 1.4 亿客户提供服务。公司通过利用数据和专有技术，开发创新产品和服务，引领行业变革。
秉承着对抗复杂性并赋能人们的使命，Nu 为客户完整的金融旅程提供服务，通过负责任的贷款和透明度促进金融准入和进步。公司由高效且可扩展的商业模式驱动，结合低成本服务与不断增长的回报。
Nu 的影响力已获得多项奖项的认可，包括《时代》100 家最具影响力公司、《快公司》最具创新力公司以及《福布斯》全球最佳银行。
访问我们的机构页面 https://www.nu.com/2026-en
Nubank 机器学习工程师
在 Nubank，机器学习工程师是我们在大规模决策中的核心。我们构建、训练和部署模型，每天为数百万客户驱动信用、欺诈、风险、个性化决策以及越来越多的 AI 原生体验。我们以工程严谨性、统计深度和对影响力的深刻关注来实现这一目标。
我们的 MLE 工程师参与完整的建模生命周期：将业务问题转化为 ML 问题，进行特征工程，训练和验证模型，并在生产环境中部署和监控它们。我们重视能够快速行动、独立运作、端到端负责自己决策并对自己质量与工艺设定高标准的小型团队。
越来越多的工作还包括生成式 AI 和代理工程。根据具体问题，我们的工程师设计并构建结合模型、工具、工作流、评估循环和人工监督的系统，以在生产环境中可靠地解决实际业务任务。
我们追求最先进的 ML 实践，目前包括多种技术。虽然我们重视熟悉这些技术的候选人，但我们也相信，有兴趣加入 Nubank 的工程师能够从我们的团队中学习。
- 大规模模型训练和实验流水线
- 为批处理和实时模型提供特征工程和特征存储
- 在生产环境中进行模型部署和服务，并通过运营和业务指标进行监控
- 大规模训练数据集的分布式数据处理
- 连续集成和部署到 AWS 和 Kubernetes
- 实验跟踪、模型版本控制、
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ABOUT NU
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.
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
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/ http://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.