Staff Machine Learning Engineer, Recommendation Systems
Nubank
Staff Machine Learning Engineer, Recommendation Systems at Nubank is a full time role based in Remote · Palo Alto, California, United States (remote). It was published on 3 August 2026 and was open at last check.
| Role | Staff Machine Learning Engineer, Recommendation Systems |
|---|---|
| Company | Nubank |
| Location | Remote · Palo Alto, California, United States (remote) |
| Employment type | Full Time |
| Published | 3 August 2026 |
| Status | Open at last check |
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.
Create your free OnJob profile to apply — we'll take you to Nubank's application after sign-up. · Posted 3 Aug 2026.
Staff Machine Learning Engineer, Recommendation Systems at Nubank — questions answered
What does the Staff Machine Learning Engineer, Recommendation Systems role at Nubank pay?
Nubank does not publish a salary on this Staff Machine Learning Engineer, Recommendation Systems listing, so OnJob shows no figure for it rather than an estimate. For what this role pays across the market, the OnJob salary guides aggregate the live listings that do disclose pay.
Is the Staff Machine Learning Engineer, Recommendation Systems role at Nubank remote?
Yes — Nubank advertises this Staff Machine Learning Engineer, Recommendation Systems role as remote, tied to Remote · Palo Alto, California, United States, and it is listed as full time work. Remote terms come from the employer's own posting, so confirm the expected working hours, timezone and any on-site days with Nubank before you apply.
Is the Staff Machine Learning Engineer, Recommendation Systems role at Nubank still open?
The Staff Machine Learning Engineer, Recommendation Systems posting at Nubank was open at OnJob's last check of the employer's careers page, having been published on 3 August 2026. OnJob re-checks source listings on each build and marks a role closed once it disappears, but listings can close without notice, so the employer's own page is the final word.
How do you apply for the Staff Machine Learning Engineer, Recommendation Systems role at Nubank?
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