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 were 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 Companys Most Innovative Companies and Forbes Worlds Best Bank. Visit our institutional page Careers at Nu - Join our team!( the Role

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 youll 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.

Youll 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. Youll partner closely with product compliance and ML teams to ensure models are auditable privacy-aware and deliver measurable business value.

Youll 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 youll 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.

You can read more about the work in the AI Core team on our blog: Responsibilities

As a Lead Machine Learning Engineer in AI Core Data Intelligence you will:

• 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.

Basic Qualifications

• 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 implementing monitoring validation and alerting systems.
• Strong problem-solving skills with the ability to address complex ambiguous problems requiring coordination across multiple teams.
• Excellent communication skills capable of explaining technical concepts to both technical and non-technical stakeholders.
• Demonstrated leadership experience including mentoring team members and contributing to technical decision-making.

Preferred Qualifications

• Experience with MLflow or similar model tracking and versioning systems.
• Knowledge of foundation models fine-tuning workflows and transformer architectures.
• Experience with data pipeline orchestration tools (Dagster Airflow or similar).
• Background in financial services or fintech understanding the unique data challenges in this domain.
• Experience working in a fast-paced high-growth environment with distributed teams.
• Track record of reducing complexity in data systems and improving developer experience for ML teams.
  
  

Our Benefits

• Opportunity of earning equity at Nu
• Medical Insurance
• Dental and Vision Insurance
• Life Insurance and AD&D
• Extended maternity and paternity leaves
• Nucleo - Our learning platform of courses
• NuLanguage - Our language learning program
• NuCare - Our mental health and wellness assistance program
• Extended maternity and paternity leaves
• 401K
• Saving Plans - Health Saving Account and Flexible Spending Account
• Work-from-home Allowance
• Relocation Assistance Package if applicable.

Work Model for this Role

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 visithttps:// Can support relocation. This role is available in any of our North American offices (Palo Alto USA; Miami USA; New York USA; Durham USA; Toronto CAN)