Backed by Sequoia, Founders Fund and Redpoint.
About the role
Setting technical direction for recommendation systems, including architecture decisions that other engineers will build on for years. Designing and building production ML systems for retrieval, ranking, and multi-objective optimization that operate at scale and under real latency constraints. You will be hands-on, regularly making coding contributions.
What they're looking for
- A strong track record building and operating large-scale ML systems in production, ideally recommendation, ranking, or personalization systems
- Experience building modern recommendation systems, e.g., learned embeddings, semantic IDs, sequence models over long user histories, and conversational recommendation systems
- Deep experience with the full ML engineering lifecycle: training, deployment, monitoring, data consistency, experimentation, and governance
- Strong software engineering fundamentals and fluency in Python and/or Scala, or equivalent languages
- Real experience with the operational side of ML: on-call, incident response, debugging systems under load
- A track record of technical leadership, whether that's an official title or just being the person a team leans on for the hard calls
More about this role
Nu serves more than 140 million customers, guided by a mission to fight complexity and empower people. The company has been leading an industry transformation through innovative products and human-centered services.
Proprietary technology and data at scale power Nu’s digital platform, built to promote financial access, advancement, and transparency. Its business model thrives on customer love and lower costs, feeding a flywheel of growth and profitability.
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We're looking for a Staff Machine Learning Engineer to help lead the technical direction of our recommendation systems. This is a hands-on senior individual contributor role for someone who has shipped ML systems at scale before and wants to shape how Nubank builds them going forward.
You'll be a technical anchor for the team, working on problems like retrieval, ranking and multi-objective optimization pipelines, and the infrastructure that lets these systems serve millions of customers with low latency and high reliability.
Setting technical direction for recommendation systems, including architecture decisions that other engineers will build on for years.
Designing and building production ML...
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