Staff Machine Learning Engineer

Nubank

Hybrid

Quick summary

Work type: Hybrid
Location: Palo Alto, CA · São Paulo, SP, Brazil
Salary: $230,000–$345,000 / yr
Posted: today

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $220k

This role $288k

$152k most similar roles pay here $366k

This role pays more than 86% of similar roles. Most pay $179,851–$259,212 — the shaded band above. At the midpoint, this role pays about $288k versus about $220k for comparable roles.

Based on 240 similar postings.

Employer

About Nubank

Nubank is one of the world's largest digital financial services companies, offering credit cards, personal loans, savings accounts, and insurance to tens of millions of customers across Brazil, Mexico, and Colombia.

Nubank currently has 11 open roles on FindRole.

Most-posted roles

View all roles at Nubank

At a glance

TL;DR · Staff Machine Learning Engineer

Apply Now Log in to save

Join Nu’s AI Core team as a Staff Machine Learning Engineer, where you will lead complex research projects and bridge the gap between cutting-edge AI and production systems. Your daily tasks include designing scalable architectures for critical use cases like credit scoring and recommendation systems, ensuring models are optimized for efficiency and cost-effectiveness while collaborating across data, infrastructure, and product teams. You should have 5-7 years of experience in applied AI/ML with deep expertise in Deep Learning frameworks such as Transformers and GNNs, strong Python coding skills, and proficiency in PyTorch or TensorFlow. Nu is one of the world’s largest digital financial platforms serving over 122 million customers across Latin America, offering a unique opportunity to drive measurable business impact at scale.

Skills

Python PyTorch TensorFlow JAX Deep Learning Transformers Graph Neural Networks MLOps A/B Testing

What you'll do

Lead and execute complex applied research initiatives independently, focusing on building scalable architectures.
Address difficult modeling problems requiring coordination across stakeholders, delivering innovative solutions with medium-term impact.
Design AI architectures that respect MLOps constraints for efficient deployment at scale.
Develop and deliver innovative solutions to project-level challenges, pushing platform improvements into production models.
Serve as a technical mentor for senior engineers and researchers, providing guidance on deep learning fundamentals.