Lead ML Data Engineer, AI Core

About Us

Nu is one of the largest digital financial platforms in the world, with more than 127 million customers across Brazil, Mexico, and Colombia. Guided by our mission to fight complexity and empower people, we are redefining financial services in Latin America and this is still just the beginning of the purple future we're building.

Listed on the New York Stock Exchange (NYSE: NU), we combine proprietary technology, data intelligence, and an efficient operating model to deliver financial products that are simple, accessible, and human.

Our impact has been recognized by global rankings such as Time 100 Companies, Fast Company's Most Innovative Companies, and Forbes World's Best Bank. Visit our institutional page Careers at Nu - Join our team!

At Nu, data is the foundation that powers our AI and machine learning models, enabling millions of customers to access fair financial products. As a Machine Learning Engineer in AI Core, Data Intelligence, you’ll work across a broad spectrum — from building scalable data infrastructure and feature pipelines that feed our state-of-the-art foundation models to designing, training, and shipping transaction classification models that power critical customer experiences across the company.

You’ll work at the intersection of data and applied machine learning, contributing across multiple stages of the ML lifecycle: ingesting and labeling data, training and evaluating models, and helping with deployment and production monitoring through robust quality controls. You’ll partner closely with product, compliance, and ML teams to ensure models are auditable, privacy‑aware, and deliver measurable business value.

You’ll join a team that manages the data engineering backbone of AI Core, ensuring data is accessible, healthy, and properly tracked across our entire ML ecosystem. Here, you’ll combine your expertise in building scalable data systems with your passion for machine learning, creating solutions that enable our models to learn from better, richer data.

• Design and build scalable data ingestion pipelines that bring new datasets into our AI Core platform, ensuring reliable, efficient data flow from source to model training.
• Implement data quality monitoring and validation systems that catch issues before they impact model performance, maintaining the health of datasets across our ML ecosystem.
• Model new types of data into our foundation models.
• Analyze the impact of new data sources on existing models, conducting experiments to measure performance improvements and guide data acquisition decisions.
• Develop and maintain data preparation workflows that transform raw data into features ready for model training, working with distributed computing frameworks like Ray.
• Tune and optimize machine learning models when new datasets are integrated, applying hyperparameter optimization and evaluating model performance improvements.
• Collaborate with AI Core ML, Platform, and Infra teams to ensure seamless data flow across our ML infrastructure, from ingestion to model deployment.
• Lead technical initiatives that improve our data engineering practices, setting standards for data quality, pipeline reliability, and model‑data integration.
• Mentor team members and contribute to hiring activities, helping build a strong and diverse team that drives innovation in AI infrastructure.

• Typically 6+ years of experience in machine learning engineering, data engineering, or related fields with a strong track record of building production data and ML systems.
• Proven experience designing and building data ingestion pipelines at scale, with expertise in distributed computing frameworks (Ray, Spark, or similar).
• Strong background in applied machine learning, including model training, hyperparameter tuning, and performance evaluation.
• Experience analyzing how data changes impact model performance, with the ability to design and run experiments to measure improvements.
• Proficiency in Python for data engineering and ML workflows, with experience working with large-scale data processing systems.
• Solid understanding of data quality principles and experience…