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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer, Recommendation Systems - **Company:** Nubank Era, LLC - **Location:** Palo Alto, CA, United States - **Experience:** Expert - **Salary:** $230,000.0 - $345,000.0 - **Contract:** Permanent contract - **Skills:** Software Debugging, Distributed Systems, Intrusion Detection Systems, Python (Programming Language), Machine Learning, Recommender Systems, Apache Spark, Technical Debt, Low Latency, Machine Learning Operations - **Published:** August 2, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=d34ee0ae95580d9d ## About the Role * 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 ## Description 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., 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 ## Related Videos - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale) - [Resolving technical debts in software architecture](https://www.wearedevelopers.com/videos/1680-resolving-technical-debts-in-software-architecture) - [Swapping Low Latency Data Storage Under High Load](https://www.wearedevelopers.com/videos/746-swapping-low-latency-data-storage-under-high-load) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Your Code as a Crime Scene](https://www.wearedevelopers.com/videos/1342-your-code-as-a-crime-scene) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)