资深机器学习工程师，推荐系统
Staff Machine Learning Engineer, Recommendation Systems
Nu 是拉丁美洲领先的数字银行，为巴西、墨西哥和哥伦比亚的 1.4 亿客户提供服务。公司通过利用数据和专有技术开发创新产品和服务，引领行业变革。
以对抗复杂性并赋能人们为使命，Nu 为客户完整的金融旅程提供服务，通过负责任的贷款和透明度促进金融准入和进步。公司采用高效且可扩展的商业模式，结合低成本服务与不断增长的回报。
Nu 的影响力已获得多项奖项的认可，包括《时代》杂志 100 家最具影响力公司、《快公司》最具创新力公司以及《福布斯》世界最佳银行。
访问我们的机构页面 https://www.nu.com/2026-en
我们正在寻找一名资深机器学习工程师，帮助指导我们推荐系统的工程技术方向。这是一个需要亲自参与的高级个人贡献者角色，适合之前已经部署过大规模 ML 系统，并希望塑造 Nubank 未来如何构建这些系统的人。
你将成为团队的技术核心，负责如检索、排序和多目标优化流程等问题，以及让这些系统以低延迟和高可靠性服务数百万客户的基础架构。
你将负责：
- 为推荐系统设定技术方向，包括其他工程师多年都将基于的架构决策。
- 设计和构建可在大规模和真实延迟限制下运行的生产 ML 系统，用于检索、排序和多目标优化。你将亲自参与，经常进行编码贡献。
- 领导团队中最具技术挑战性的项目，从最初设计到生产上线。
- 与应用科学家合作，将模型从研究阶段转化为可靠、受监控的生产系统。
- 提升团队的技术标准：审查设计方案，指导工程师，并推动测试、实验、监控和系统设计方面的更好实践。
- 直接与利益相关者团队合作，了解他们的推荐需求，并将其转化为共享、可重用的基础设施，而不是一次性解决方案。
- 识别并解决阻碍团队效率的结构性问题，无论是工具、流程还是技术债务。
我们寻找的人选应具备：
- 强烈的...
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ABOUT NU
Nu is the leading digital bank in Latin America, serving 140 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 https://www.nu.com/2026-en
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.