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Own the full ML lifecycle—from framing ambiguous business problems, engineering features, training and validating models, to deploying and monitoring them in batch and real‑time production.
Strong foundation in statistics and ML theory, solid Python coding with libraries like scikit‑learn, PyTorch or TensorFlow, SQL skills, and the ability to communicate with both technical and business stakeholders.
Your models power credit, fraud, risk and personalization decisions for over 140 million customers, making ML the core engine of Nubank’s mission to simplify finance and drive growth.
Engineers who thrive in small, fast‑moving teams, enjoy end‑to‑end production ML work, mentor peers, and are eager to learn and apply modern MLOps and cloud tooling.
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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Machine Learning Engineer at Nubank
At Nubank, Machine Learning Engineers sit at the core of how we make decisions at scale. We build, train, and deploy models that drive credit, fraud, risk, personalization decisions and a growing set of AI-native experiences for millions of customers every day. We do it with engineering rigor, statistical depth, and a deep focus on impact.
Our MLEs work across the full modeling lifecycle: framing business problems as ML problems, engineering features, training and validating models, and deploying and monitoring them in production. We value small, independent teams that move fast, own their decisions end-to-end, and hold themselves to a high bar for quality and craft.
Increasingly, that work also includes Generative AI and Agentic Engineering. Depending on the problem, our engineers design and build systems that combine models, tools, workflows, evaluation loops, and human oversight to solve real business tasks reliably in production.
We strive for state-of-the-art ML practices that currently include a variety of technologies. While we value candidates that are familiar with them, we are also confident that engineers who are interested in joining Nubank will be able to learn from our team.
What We're Looking For
Knowledge of software engineering best practices: testing, clean code, documentation
Work Model
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 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
Nubank
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