AWSGCPCloudPythonMLAIStaff ML EngineerModel DevelopmentDeploymentOptimization
📋 Job Overview
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.
🏢 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.
🎯 The Role
As a Machine Learning Engineer, you’re expected to directly contribute to Nu Colombia’s strategy and performance mainly by building forecasting models and models to assess and decide on marketing investments. You will 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, and lead modeling projects end-to-end.
✅ Key Responsibilities
Directly contribute to Nu Colombia’s strategy and performance mainly by building forecasting models and models to assess and decide on marketing investments
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
Translate business needs into modeling strategies aligned with Nubank's architecture and long-term goals
📌 Required Qualifications
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
⭐ Desirable Experience
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
🎁 Benefits
Chance of earning equity at Nu
Extended maternity and paternity leaves
Health and life insurance
NuCare - Our mental health and wellness assistance program
Nucleo - Our learning platform of courses
NuLanguage - Our language learning program
Vacations of 15 workdays
Gym partnership
Work-from-home Allowance
Parental Consultancy
Relocation Assistance Package, if applicable
🛂 Visa & Eligibility
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.
