About the teamAt Nubank we heavily rely on Data, Machine Learning, and increasingly on Generative and Agentic AI to drive our strategy and deliver the best experience and products to our customers. The Model Risk team plays a crucial role in ensuring the risks associated with our models and AI systems are understood and under control. We are now building a dedicated AI Risk Management capability to address the emerging risks of advanced AI - including LLM-powered and autonomous agentic systems - with a focus on AI quality, model and agent behavior, and the platform controls that keep these systems safe and reliable across internal and customer-facing use cases.
About the roleThis is a senior, hands-on technical position. You will help define what model risk management looks like for AI and Agentic AI at Nubank - building and enhancing the frameworks, not just inheriting them. You will perform independent assessments of AI systems for quality, behavior, and robustness, and help design the guardrails and platform-level controls that govern their safe use. You'll act as a credible technical peer to first-line engineering and AI development teams, providing practical guidance on AI risk without slowing responsible innovation. This role focuses on AI quality, agentic behavior, and platform controls; cybersecurity, data privacy, and fraud-specific matters sit with partner functions and are out of scope.
What you will doAI Risk Framework & Governance
- Build and continuously enhance the risk management framework for AI and Agentic AI systems, including inventory standards, assessment methodologies, control design, and issue management.
- Inventory and map Nubank's AI use cases to surface gaps, materiality, and the most critical risks, and define prioritized mitigation actions.
- Assess whether first-line monitoring is effective, proportionate to model risk, and sufficient to keep AI systems fit for purpose over time.
Independent AI Assessment
- Perform independent technical assessments across generative AI, and agentic workflows, covering data, assumptions, methodology, testing, behavior, and monitoring.
- Assess risks in LLM-powered applications, including RAG pipelines, tool use, autonomy boundaries, model/agent quality, human oversight, and hallucination risk.
- Identify and document model limitations, failure modes, and emerging AI risks, including drift, instability, fairness, and robustness concerns.
Controls, Platform & Enablement
- Influence first-line teams on platform architecture and embedded controls for the safe deployment and monitoring of AI.
- Build Key Risk Indicators (KRIs) and metrics for continuous monitoring of AI risk.
- Develop tools, evals, analyses, and playbooks (including AI-enabled automation) to improve the speed, scale, and effectiveness of AI governance and validation.
Advisory & Advocacy
- Serve as a trusted advisor across the AI/ML lifecycle, evaluating new use cases for materiality and governance requirements prior to deployment.
- Discuss and report AI risk status and independent opinions to stakeholders, including senior managers and, where relevant, regulators.
- Champion AI risk management as a strategic enabler of safe and scalable AI adoption, and build AI risk literacy across engineering, product, and risk teams.
- Work in a multicultural, diverse, and highly skilled environment.
Requirements we are looking for- Education: A bachelor's or master's degree in a quantitative field (computer science, data science, statistics, mathematics, engineering, or related).
- Hands-on AI/ML experience: A track record developing or validating AI/ML models and systems, ideally a candidate who has moved from an AI / Machine Learning Engineer background into model risk, governance, or risk management. You don't need to have trained foundation models from scratch, but you need solid, current technical depth.
- Strong technical foundations: Proficiency in Python, SQL, and modern ML tooling; familiarity with LLMs, RAG systems, prompt engineering, and AI agent frameworks.
- Evaluation and testing: Experience evaluating and testing ML and generative AI systems, including custom evals, benchmarking, stress testing, and drift/degradation monitoring.
- Risk management experience: Demonstrated experience in risk identification, control definition, and framework building; understanding of model risk governance principles and independent effective challenge.
- Data skills: Experience working with large datasets and building dashboards and analyses to support risk visibility.
- High agency and adaptability: Comfortable operating in ambiguity, synthesizing fragmented technical and business context into a clear view of how complex AI systems actually work, and making sound judgments without a complete playbook.
- Influencing skills: Able to engage and align stakeholders across first and second lines of defense as a credible technical peer.
- Communication: Strong written and verbal skills, you can explain AI risk to a data scientist and to a regulator, and use different language for each.
- Advanced or fluent English: You will meet with partners and stakeholders across countries and prepare documentation and presentations in English.
- PLUS: Experience in a 2nd or 3rd line of defense.
- PLUS: Familiarity with regulatory Model Risk Management and AI frameworks (e.g., SR 11-7 / SR 26-2 / OCC 2011-12, NIST AI RMF, EU AI Act).
Total compensation includes base salary, RSUs and benefits. Base salary range: US$108k - US$131k.
Benefits- Opportunity of earning equity at Nu
- 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.
Work Model for this RoleHybrid 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.
