Lead/Staff Machine Learning Engineer

About us

Nu was born in 2013 with the mission to fight complexity to empower people in their daily lives by reinventing financial services. We are one of the world’s largest digital banking platforms, serving millions of customers across Brazil, Mexico, and Colombia.

ML Engineering at Nubank

Data Science is essential to every part of the business at Nubank, since Day 1. Most areas of the company use data science to some extent. ML Engineers are key to connecting ML models and ML-enabled systems to the rest of our infrastructure, while making sure that they work properly, delivering value to our customers.

As a Machine Learning Engineer, you’re expected to

• Interact with other team members to understand how to best apply data science and artificial intelligence to Nubank’s problems;
• Help monitor and maintain models and ML systems, making sure they are working well as measured by performance, operational and business metrics;
• Help deploy ML models both in batch and real-time scenarios. This may include building pipelines, constructing features and integrating models to other systems;
• Help build and maintain tooling and platforms to scale operations across multiple teams;
• Be familiar with modeling and statistics concepts.

Our ideal candidate

• Has extensive programming experience and is familiar with modern development techniques;
• Has experience creating, deploying and maintaining ML models and ML-enabled systems using modern frameworks;
• Understands the most common failure modes in ML-enabled systems and knows how to avoid and/or mitigate those;
• Is familiar with all stages of the ML model lifecycle, from inception to deployment and steady-state operation;
• Is familiar with cloud-based services from vendors such as AWS, GCP and Azure.
• Is familiar with software engineering and architecture principles and how they enable sustainable and maintainable systems;
• Is familiar with data engineering routines, pipelines and job scheduling frameworks;
• Is able to navigate complex mathematical and analytical environments when needed.

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.