Lead Machine Learning Engineer / Applied AI Scientist

Benefits

Parental leave

Not verified

Non-birth-parent leave

Not verified

Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

Not verified

Relocation assistance

Not verified

Childcare support

Not verified

Learning budget

Not verified

Verification

Not verified

Salary

Not verified

Was this benefit information wrong? Tell us.

Schedule

Shift type

Not verified

Weekend work

Not verified

Application

Cover letter

Not verified

Assessment

Not verified

Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Lead Machine Learning Engineer / Applied AI Scientist Brazil, Sao Paulo 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. About the role At AI Core, we are scaling the impact of our AI initiatives to become the primary driver of Nubank's most critical decision systems. We are seeking an Lead Machine Learning Engineer (Applied AI Scientist) to lead high-impact research projects that bridge the gap between state-of-the-art AI and production-grade financial systems. You will be responsible for solving complex, ambiguous problems using Deep Learning and Foundation Models, ensuring our architectures are scalable, efficient, and driving measurable business results. As an Applied AI Scientist (MLE), you're expected to: - Research Execution & Technical Leadership (Complexity & Autonomy) - Lead and execute complex applied research initiatives independently, focusing on building and optimizing architectures (e.g., Transformers, GNNs) that can be deployed across critical use cases like Credit, RecSys, GenAI, and real-time inference. - Address difficult and ambiguous modeling problems that require coordination across various stakeholders (Data, Infra, Product), delivering innovative solutions with a clear focus on medium-term impact. - Bridge the gap between research and production by designing architectures that respect MLOps constraints, ensuring models are optimized for latency, interpretability, and cost-efficiency. - Strategic Impact & Collaboration (Impact) - Develop and deliver innovative solutions that address project-level challenges,

Read the full description at job-boards.greenhouse.io. FewerJobs shows a source-linked preview and links to the original posting.