Staff Machine Learning Engineer, Recommendation Systems

Nubank; United States; Hybrid; USD 230,000 - 345,000; Full time

Benefits offered by this job

Equity participation

Medical Insurance

Dental and Vision

Life Insurance

Work-from-home Allowance

Relocation Assistance

Job summary

Nubank is seeking a Staff Machine Learning Engineer to help shape the technical direction of our recommendation systems. This hands-on senior individual contributor role is for someone who has shipped ML systems at scale and wants to influence how Nubank builds them going forward.

You’ll be a technical anchor for the team, working on retrieval, ranking and multi-objective optimization pipelines, and the infrastructure that serves millions of customers with low latency and high reliability.

Qualifications

• Proven track record building large-scale ML systems in production.
• Experience with recommendation, ranking, or personalization systems.
• Fluent in Python and/or Scala and strong software fundamentals.
• Experience with distributed data processing and monitoring.

Responsibilities

• Set the technical direction for Nubank's recommendation systems.
• Design and build production ML pipelines for retrieval, ranking, and multi-objective optimization.
• Lead technically demanding projects from design to rollout.
• Partner with data scientists to productionize models and establish governance.
• Mentor engineers and raise the bar for testing, monitoring, and system design.
• Collaborate with stakeholders to translate needs into reusable infrastructure.
• Address tooling, process, and technical debt to improve team velocity.

Skills

ML engineering

Python

Scala

Distributed systems

Leadership

Communication

Incident response

Mentoring

Ambiguity management

Education

Bachelor's degree in Computer Science or related field

Tools

Spark

Job description

About Nu

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. Visit our Institutional Page

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.