Staff Machine Learning Engineer, Recommendation Systems

Nubank•Palo Alto, CA

5d•$230,000 - $345,000•Hybrid

About The Position

Nu is the leading digital bank in Latin America, serving 135 million customers across Brazil, Mexico, and Colombia. The company has been leading an industry transformation by leveraging data and proprietary technology to develop innovative products and services. Guided by its mission to fight complexity and empower people, Nu caters to customers’ complete financial journey, promoting financial access and advancement with responsible lending and transparency. The company is powered by an efficient and scalable business model that combines low cost to serve with growing returns. Nu’s impact has been recognized in multiple awards, including Time 100 Most Influential Companies, Fast Company’s Most Innovative Companies, and Forbes World’s Best Banks. 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.

Requirements

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.
Comfort working with ambiguity and translating loose business goals into concrete technical priorities.
Good communication skills. You'll need to explain technical tradeoffs to both engineers and non-technical stakeholders.

Nice To Haves

Experience with distributed systems, Spark, or similar large-scale data processing tools is a plus.

Responsibilities

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.
Leading the most technically demanding projects on the team, from first design through production rollout.
Partnering with applied scientists to move models from research into reliable, monitored production systems.
Raising the technical bar for the team: reviewing designs, mentoring engineers, and pushing for better practices around testing, experimentation, monitoring, and system design.
Working directly with stakeholder teams to understand their recommendation needs and translate them into shared, reusable infrastructure rather than one-off solutions.
Identifying and fixing the structural issues that slow the team down, whether that's tooling, process, or technical debt.