Staff Machine Learning Engineer, Recommendation Systems

About Nu

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.

You'll be responsible for

• 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.

We're looking for someone who has

• 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.

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

  • Opportunity of earning equity at Nu

  • Total compensation includes base salary, RSUs and benefits. Base salary range: $230k - $345k

  • Medical Insurance

  • Dental and Vision Insurance

  • Life Insurance and AD&D

  • Extended maternity and paternity leaves

  • Nucleo - Our learning platform of courses

  • NuLanguage - Our language learning program

  • NuCare - Our mental health and wellness assistance program

  • Extended maternity and paternity leaves

  • 401K

  • Saving Plans - Health Saving Account and Flexible Spending Account

  • Work-from-home Allowance

  • Relocation Assistance Package, if applicable.

  Role Location

  Palo Alto, California
  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. For more details, visit https://building.nubank.com/nu-hybrid-work-model/

Our recruitment process may involve the use of artificial intelligence–enabled tools, such as automated interview transcription and analysis, to support the evaluation process. Artificial intelligence is not used to make final hiring decisions; all decisions are made by human reviewers.

To maintain a consistent and fair process for every candidate, Nu does not provide individualized technical feedback. See how our policy works here