Nubank

<h2>About Nu</h2>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<a target="_blank" rel="noopener noreferrer ugc" href="https://www.nu.com/2026-en"> Institutional Page</a><h3> About the role</h3>At AI Core, we are scaling the impact of our AI initiatives to become the primary driver of Nubank’s most critical decision systems. We are seeking  Machine Learning Engineers to lead high-impact research projects that bridge the gap between state-of-the-art AI and production-grade financial systems. You will be responsible for solving complex, ambiguous problems using Deep Learning and Foundation Models, ensuring our architectures are scalable, efficient, and driving measurable business results.As an Machine Learning Engineer (MLE), you’re expected to:<ol><li>Research Execution &amp; Technical Leadership (Complexity &amp; Autonomy)</li></ol><ul><li>Lead and execute complex applied research initiatives independently, focusing on building and optimizing architectures (e.g., Transformers, GNNs) that can be deployed across critical use cases like Credit, RecSys, GenAI, and real-time inference.</li><li>Address difficult and ambiguous modeling problems that require coordination across various stakeholders (Data, Infra, Product), delivering innovative solutions with a clear focus on medium-term impact.</li><li>Bridge the gap between research and production by designing architectures that respect MLOps constraints, ensuring models are optimized for latency, interpretability, and cost-efficiency.</li></ul><ol><li>Strategic Impact &amp; Collaboration (Impact)</li></ol><ul><li>Develop and deliver innovative solutions that address project-level challenges, focusing on pushing the latest platform and AI research improvements into downstream production models.</li><li>Actively participate in cross-functional collaborations, ensuring that research outputs are seamlessly integrated into Nubank's decision-making engines.</li><li>Establish technical standards within the AI Core team for experimentation, model evaluation, and code quality, inspiring peers to raise their performance.</li></ul><ol><li>Mentorship &amp; Function Contribution (Function Contribution)</li></ol><ul><li>Serve as a technical mentor for senior engineers and researchers, providing guidance on deep learning fundamentals, problem formulation, and research methodology.</li><li>Actively contribute to the function's growth by participating in mandatory activities like hiring (interview panels) and leading internal task forces to improve our ML lifecycle.</li><li>Contribute to thought leadership by participating in research collaborations or internal papers that align with Nubank’s strategic goals.</li></ul>What are we looking for?<ul><li>Professional Experience: 5-7+ years in applied AI/ML, with a proven track record of delivering research-driven systems into production environments</li><li>Technical Mastery:<ul><li>Deep expertise in Deep Learning architectures (Transformers, Multimodal, or GNNs).</li><li>Strong coding skills in Python and proficiency with frameworks like PyTorch, JAX, or TensorFlow.</li><li>Solid understanding of MLOps and the constraints of deploying models at scale.</li></ul></li><li>Problem Solving: Sophisticated skills in ML problem formulation and the ability to navigate uncertainty when data is messy or unavailable.</li><li>Communication: Ability to communicate complex technical concepts to both technical peers and cross-functional stakeholders, ensuring alignment and buy-in.</li><li>Analytical Capacity: Experience with large-scale experimentation and A/B testing to validate research hypotheses.</li></ul>Our Benefits<ul><li>Opportunity of earning equity at Nu</li><li>Total compensation includes base salary, RSUs and benefits. Base salary range: $230k - $345k</li><li>Medical Insurance</li><li>Dental and Vision Insurance</li><li>Life Insurance and AD&amp;D</li><li>Extended maternity and paternity leaves </li><li>Nucleo - Our learning platform of courses</li><li>NuLanguage - Our language learning program</li><li>NuCare - Our mental health and wellness assistance program</li><li>Extended maternity and paternity leaves </li><li>401K</li><li>Saving Plans - Health Saving Account and Flexible Spending Account</li><li>Work-from-home Allowance</li><li>Relocation Assistance Package, if applicable.</li></ul>Role LocationPalo 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 <a target="_blank" rel="noopener noreferrer ugc" href="https://building.nubank.com/nu-hybrid-work-model/">https://building.nubank.com/nu-hybrid-work-model/</a> 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.

Staff Machine Learning Engineer

<h2>About Nu</h2>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<a target="_blank" rel="noopener noreferrer ugc" href="https://www.nu.com/2026-en"> Institutional Page</a> At AI Core, we are scaling the impact of our AI initiatives to become the primary driver of Nubank’s most critical decision systems. We are seeking an Lead Machine Learning Engineer (Applied AI Scientist) to lead high-impact research projects that bridge the gap between state-of-the-art AI and production-grade financial systems. You will be responsible for solving complex, ambiguous problems using Deep Learning and Foundation Models, ensuring our architectures are scalable, efficient, and driving measurable business results.As an Applied AI Scientist (MLE), you’re expected to:<ol><li>Research Execution &amp; Technical Leadership (Complexity &amp; Autonomy)</li></ol><ul><li>Lead and execute complex applied research initiatives independently, focusing on building and optimizing architectures (e.g., Transformers, GNNs) that can be deployed across critical use cases like Credit, RecSys, GenAI, and real-time inference.</li><li>Address difficult and ambiguous modeling problems that require coordination across various stakeholders (Data, Infra, Product), delivering innovative solutions with a clear focus on medium-term impact.</li><li>Bridge the gap between research and production by designing architectures that respect MLOps constraints, ensuring models are optimized for latency, interpretability, and cost-efficiency.</li></ul><ol><li>Strategic Impact &amp; Collaboration (Impact)</li></ol><ul><li>Develop and deliver innovative solutions that address project-level challenges, focusing on pushing the latest platform and AI research improvements into downstream production models.</li><li>Actively participate in cross-functional collaborations, ensuring that research outputs are seamlessly integrated into Nubank's decision-making engines.</li><li>Establish technical standards within the AI Core team for experimentation, model evaluation, and code quality, inspiring peers to raise their performance.</li></ul><ol><li>Mentorship &amp; Function Contribution (Function Contribution)</li></ol><ul><li>Serve as a technical mentor for senior engineers and researchers, providing guidance on deep learning fundamentals, problem formulation, and research methodology.</li><li>Actively contribute to the function's growth by participating in mandatory activities like hiring (interview panels) and leading internal task forces to improve our ML lifecycle.</li><li>Contribute to thought leadership by participating in research collaborations or internal papers that align with Nubank’s strategic goals.</li></ul>What are we looking for?<ul><li>Professional Experience: 5-7+ years in applied AI/ML, with a proven track record of delivering research-driven systems into production environments</li><li>Technical Mastery:<ul><li>Deep expertise in Deep Learning architectures (Transformers, Multimodal, or GNNs).</li><li>Strong coding skills in Python and proficiency with frameworks like PyTorch, JAX, or TensorFlow.</li><li>Solid understanding of MLOps and the constraints of deploying models at scale.</li></ul></li><li>Problem Solving: Sophisticated skills in ML problem formulation and the ability to navigate uncertainty when data is messy or unavailable.</li><li>Communication: Ability to communicate complex technical concepts to both technical peers and cross-functional stakeholders, ensuring alignment and buy-in.</li><li>Analytical Capacity: Experience with large-scale experimentation and A/B testing to validate research hypotheses.</li></ul><div> </div>Our Benefits<ul><li>Chance of earning equity at Nubank</li><li>Food/ Meal Card (Vale-Refeição and/or Vale Alimentação)</li><li>Public Transportation Commuting Benefit (Vale-Transporte)</li><li>NuCare – Psychological, Financial and Legal Assistance Program</li><li>Life Insurance</li><li>Medical Plan</li><li>Dental Plan</li><li>NuLanguage – Language Course Program</li><li>Nucleo - Our learning platform of courses</li><li>Extended Parental Leave</li><li>Daycare Allowance</li><li>Parental Consultancy</li><li>Work-from-home Allowance</li><li>Gym Partnerships</li><li>30 days of paid vacation</li><li>Relocation Assistance Package, if applicable</li></ul><h3>Work Model for this Role</h3>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 <a target="_blank" rel="noopener noreferrer ugc" href="https://building.nubank.com/nu-hybrid-work-model/">https://building.nubank.com/nu-hybrid-work-model/</a>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.

Lead Machine Learning Engineer / Applied AI Scientist

<h2>About Nu</h2>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<a target="_blank" rel="noopener noreferrer ugc" href="https://www.nu.com/2026-en"> Institutional Page</a><h2> About the role</h2>As AI Research Manager, you will own the operating health and execution of Nubank's ML research agenda. This is a dedicated people-management role for a small, high-talent-density team of world-class researchers, partnering closely with senior technical leads who drive the scientific direction. The team works on a portfolio of research bets on a one-to-four-quarter horizon. This work compounds into the production capabilities Nubank will depend on. Current and upcoming bets include:<ul><li>Next-generation nuFormer architectures: proprietary transformers that learn from raw transaction sequences and power key production credit decisions</li><li>Multitask and multi-target modeling: single models serving many high-impact prediction tasks at once</li><li>Training and inference efficiency: distillation, quantization, sparsity, and parallelism to run state-of-the-art models economically at our scale</li><li>Causal modeling and policy optimization: moving beyond prediction to the decisions and policies those predictions should drive</li><li>World models: open-ended models that reason about a customer's full financial life</li><li>Recommendation systems: extending the backbone to app events, engagement, and personalization signals</li><li>Embeddings and representation learning: semantic IDs, contrastive learning, and reusable representations used across the bank</li><li>Real-time and continual learning: low-latency inference and models that adapt over time</li></ul>Your job is to make exceptional science happen: build the conditions, focus, and operating rhythm that researchers do great work. Location: Palo Alto, US<h2>You'll be responsible for</h2><h3>People &amp; Team Leadership</h3><ul><li>Lead, mentor, and advocate for a team of world-class ML researchers, fostering an environment of psychological safety, high ambition, and rigorous scientific inquiry.</li><li>Own the operating health of the team, including performance, career growth, hiring, and compensation cycles for elite individual contributors who often operate at staff-and-above technical depth.</li><li>Attract and retain top-tier research talent in a competitive market, and build a reputation for the team as a place the best researchers want to be.</li></ul><h3>Research Operations &amp; Execution</h3><ul><li>Own the operating cadence of a portfolio of two-to-three concurrent, quarter-scale research bets, from problem framing and OKRs through progress tracking and clear go/no-go decisions.</li><li>Allocate scarce, high-value resources, most notably GPU capacity, across competing research priorities, balancing exploration against the bets most likely to compound.</li><li>Protect deep-focus research time. Sustaining a long-term agenda in a fast-moving company means deliberately creating the space for rigorous, multi-quarter work, so the team can pursue ambitious bets instead of being fully absorbed by short-term applied demands.</li><li>Raise the bar on research rigor and communication: strong experimental design, peer review, and reproducibility.</li><li>Cultivate the team's standing in the broader research community. Real-world impact is our primary measure of success, but we actively encourage publishing, open-source contribution, and conference presence that build a reputation reflecting the quality of the work and help attract the best researchers.</li></ul><h3>Strategy &amp; Cross-Team Collaboration</h3><ul><li>Partner closely with senior technical leads who drive architecture and scientific direction, aligning operational execution with the long-term research roadmap so that scientific and operating decisions reinforce each other.</li><li>Ensure clean handoffs from research into production, so breakthrough results land reproducibly in the hands of applied teams rather than stalling as one-off experiments.</li><li>Translate complex, frontier technical progress (training efficiency, causal inference, novel architectures) into clear, high-impact narratives for executive leadership, connecting research milestones to business outcomes.</li><li>Bridge foundational research and long-term business strategy, ensuring the team's technical inputs directly enable Nubank's AI-first direction.</li></ul><h2>We are looking for a person who has</h2>You are an experienced research or engineering manager working at the intersection of cutting-edge AI research and Applied ML. You lead brilliant, highly autonomous researchers without micromanaging, and you know what it takes to sustain an ambitious, long-horizon research agenda inside a fast-moving company. You measure your success by what your team ships and discovers, not by your own keystrokes.<h3>Basic Qualifications:</h3><ul><li>Experience: 3+ years of direct people-management experience leading applied AI research or core machine learning teams at big tech companies, frontier labs, or comparable high-scale environments.</li><li>Education: M.S. or Ph.D. in Computer Science, Machine Learning, Applied Mathematics, or a related quantitative field, with a strong working understanding of core AI methodologies.</li><li>Track record: Proven ability to build, scale, and retain high-performing research or advanced applied-science teams.</li></ul><h3>Preferred Qualifications:</h3><ul><li>Technical depth: This is a management-first role, but deep conceptual familiarity with at least one of our core research areas is highly valued: Foundation models &amp; LLMs: pre-training from scratch, scaling laws, training-optimization frameworks, and large GPU-cluster workflows. Behavioral &amp; sequential models: sequence models, recommendation systems, and large-scale representation learning for very large user bases. Decisioning &amp; optimization: causal inference, policy optimization, constrained optimization, or reinforcement learning. Training &amp; inference efficiency: model sparsification, quantization, distillation, or parallelism and partitioning design.</li><li>Communication: Exceptional storytelling skills, with a track record of translating complex technical milestones into business impact for senior and C-level audiences.</li><li>Environment: Experience operating in global, distributed teams and navigating the path from foundational research to scalable, production-grade systems.</li></ul><h3>Why This Role</h3>This is a rare chance to lead frontier AI research with a clear, compounding mission: building the intelligence layer of one of the world's largest digital banks. You'll work alongside strong ML researchers, with the data, compute, and executive backing to pursue genuinely ambitious bets, and to see those bets reach more than 130 million customers.<h3>Location</h3>Palo Alto, United States<div> </div><h3>Our Benefits</h3><ul><li>Total compensation includes base salary, RSUs and benefits. Base salary: $324k</li><li>Opportunity of earning equity at Nu</li><li>Medical Insurance</li><li>Dental and Vision Insurance</li><li>Life Insurance and AD&amp;D</li><li>Extended maternity and paternity leaves </li><li>Nucleo - Our learning platform of courses</li><li>NuLanguage - Our language learning program</li><li>NuCare - Our mental health and wellness assistance program</li><li>401K</li><li>Saving Plans - Health Saving Account and Flexible Spending Account</li><li>Work-from-home Allowance</li><li>Relocation Assistance Package, if applicable.</li></ul><h3>Work Model for this Role</h3>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 <a target="_blank" rel="noopener noreferrer ugc" href="https://building.nubank.com/nu-hybrid-work-model/">https://building.nubank.com/nu-hybrid-work-model/</a> 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.

AI Research Manager - Machine Learning

<h2>About Nu</h2>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<a target="_blank" rel="noopener noreferrer ugc" href="https://www.nu.com/2026-en"> Institutional Page</a><h2>About the Role</h2>We are looking for an engineer who has already built with AI in production. You have shipped LLM-powered systems (agents, copilots, RAG pipelines, or AI-driven automations), you know what breaks when they meet real users, and you know how to make them reliable enough for business-critical workflows.Your day-to-day is applied AI engineering: designing agentic workflows, integrating LLMs into internal tools and business processes, building evaluation and guardrail layers, and turning manual, high-friction workflows into AI-assisted ones that thousands of Nubankers depend on.This is not an infrastructure role. You will not spend your days on Terraform, IAM policies, or email deliverability. Cloud fluency helps, but the core of this job is the AI layer — prompts, context, agents, evaluations, integrations — and the product judgment to know where AI genuinely helps versus where deterministic automation is the right answer.<h3>Key Responsibilities</h3>Applied AI &amp; Agentic Systems<ul><li>Design, build, and ship LLM-powered agents and workflows that automate complex internal processes end-to-end.</li><li>Work hands-on with frontier models and the modern AI stack: tool/function calling, structured outputs, MCP, RAG, multi-agent orchestration.</li><li>Own the full lifecycle of an AI system: from problem discovery and prototype to production hardening, monitoring, and iteration.</li></ul>Evaluation &amp; Reliability<ul><li>Build evaluation harnesses, guardrails, and quality feedback loops so AI systems can be trusted in production — not just demoed.</li><li>Define what "good" looks like for non-deterministic systems and instrument it: evals, regression suites, human-in-the-loop review where it matters.</li></ul>Intelligent Workflow Automation<ul><li>Use orchestration platforms (e.g., n8n) and custom integrations as delivery vehicles for AI-in-the-loop automation across business units.</li><li>Integrate enterprise platforms (Slack, Google Workspace, Jira, internal APIs) into coherent, AI-assisted workflows.</li></ul>AI Adoption &amp; Governance<ul><li>Drive the technical strategy for AI adoption within engineering and business workflows.</li><li>Develop governance frameworks that make AI coding assistants and agents safe, compliant, and effective — balancing developer freedom with security and operational risk.</li></ul>Multiplier Work<ul><li></li></ul><ul><li>Create Golden Paths, reference implementations, and documentation that let other teams build AI workflows safely on their own.</li><li>For Lead/IC6: act as the technical reference for applied AI in the domain, influence architecture beyond the immediate team, mentor senior engineers, and partner with ITSec and Privacy to align AI solutions with company policy.</li><li>For Senior/IC5: execute complex AI projects with high autonomy, identify workflow bottlenecks worth automating, and mentor mid-level engineers.</li></ul><h3>What are we looking for? </h3>Must Have — Demonstrated Applied AI Experience<ul><li>Shipped LLM systems in production: at least one real system with an LLM at its core — an agent, copilot, RAG application, or AI-driven automation — used by real users, not a proof of concept.</li><li>Hands-on AI engineering: practical fluency with prompt and context engineering, tool/function calling, structured outputs, and agent frameworks or orchestration patterns.</li><li>Evaluation mindset: experience measuring and improving AI output quality — evals, test sets, feedback loops — and an honest understanding of failure modes (hallucination, drift, prompt injection).</li><li>Solid software engineering foundation: proficiency in Python, TypeScript, or Clojure; strong API and integration skills; the discipline to ship maintainable systems, not notebooks.</li><li>AI product sense: the judgment to identify which problems deserve an LLM, which need deterministic automation, and which should not be automated at all.</li></ul>Nice to Have<ul><li>Experience with workflow automation platforms (n8n, Zapier, or custom orchestration engines).</li><li>Exposure to cloud services (AWS) and infrastructure-as-code.</li><li>Familiarity with AI developer tooling (Claude Code, Cursor, Copilot) and AI governance practices.</li></ul>Behavioral &amp; Strategic Skills<ul><li>Builder bias: you prototype fast, validate with real users, and harden what works.</li><li>Governance-aware: you understand that "efficiency" must be balanced with "security," and you can design AI systems that are safe by default without destroying velocity.</li><li>Multiplier: you enjoy documenting your work, creating Golden Paths, and teaching others how to use what you build.</li><li>Comfortable with ambiguity: AI capabilities shift monthly; you treat that as an opportunity to re-solve problems better, not as churn.</li></ul>Location<ul><li>Miami, United States</li><li>Palo Alto, United States</li><li>Washington DC, United States</li></ul><h2>Our Benefits</h2><ul><li>Opportunity of earning equity at Nu</li><li>Medical Insurance</li><li>Dental and Vision Insurance</li><li>Life Insurance and AD&amp;D</li><li>Extended maternity and paternity leaves </li><li>Nucleo - Our learning platform of courses</li><li>NuLanguage - Our language learning program</li><li>NuCare - Our mental health and wellness assistance program</li><li>401K</li><li>Saving Plans - Health Saving Account and Flexible Spending Account</li><li>Work-from-home Allowance</li><li>Relocation Assistance Package, if applicable.</li></ul><h2>Work Model for this Role</h2>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<a target="_blank" rel="noopener noreferrer ugc" href="https://building.nubank.com/nu-hybrid-work-model/"> https://building.nubank.com/nu-hybrid-work-model/</a> Location-Specific Disclosures<ul><li>Palo Alto: Total compensation includes base salary, RSUs and benefits. Base salary range: $11,712 - $17,568</li></ul>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.

Lead AI Engineer — Productivity Systems

<h2>About Nu</h2>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<a target="_blank" rel="noopener noreferrer ugc" href="https://www.nu.com/2026-en"> Institutional Page</a><h2> About the Role</h2>We are looking for an engineer who has already built with AI in production. You have shipped LLM-powered systems (agents, copilots, RAG pipelines, or AI-driven automations), you know what breaks when they meet real users, and you know how to make them reliable enough for business-critical workflows.Your day-to-day is applied AI engineering: designing agentic workflows, integrating LLMs into internal tools and business processes, building evaluation and guardrail layers, and turning manual, high-friction workflows into AI-assisted ones that thousands of Nubankers depend on.This is not an infrastructure role. You will not spend your days on Terraform, IAM policies, or email deliverability. Cloud fluency helps, but the core of this job is the AI layer — prompts, context, agents, evaluations, integrations — and the product judgment to know where AI genuinely helps versus where deterministic automation is the right answer.<h3>Key Responsibilities</h3>Applied AI &amp; Agentic Systems<ul><li>Design, build, and ship LLM-powered agents and workflows that automate complex internal processes end-to-end.</li><li>Work hands-on with frontier models and the modern AI stack: tool/function calling, structured outputs, MCP, RAG, multi-agent orchestration.</li><li>Own the full lifecycle of an AI system: from problem discovery and prototype to production hardening, monitoring, and iteration.</li></ul>Evaluation &amp; Reliability<ul><li>Build evaluation harnesses, guardrails, and quality feedback loops so AI systems can be trusted in production — not just demoed.</li><li>Define what "good" looks like for non-deterministic systems and instrument it: evals, regression suites, human-in-the-loop review where it matters.</li></ul>Intelligent Workflow Automation<ul><li>Use orchestration platforms (e.g., n8n) and custom integrations as delivery vehicles for AI-in-the-loop automation across business units.</li><li>Integrate enterprise platforms (Slack, Google Workspace, Jira, internal APIs) into coherent, AI-assisted workflows.</li></ul>AI Adoption &amp; Governance<ul><li>Drive the technical strategy for AI adoption within engineering and business workflows.</li><li>Develop governance frameworks that make AI coding assistants and agents safe, compliant, and effective — balancing developer freedom with security and operational risk.</li></ul>Multiplier Work<ul><li>Create Golden Paths, reference implementations, and documentation that let other teams build AI workflows safely on their own.</li><li>For Lead/IC6: act as the technical reference for applied AI in the domain, influence architecture beyond the immediate team, mentor senior engineers, and partner with ITSec and Privacy to align AI solutions with company policy.</li><li>For Senior/IC5: execute complex AI projects with high autonomy, identify workflow bottlenecks worth automating, and mentor mid-level engineers.</li></ul><h3>What are we looking for? </h3>Must Have — Demonstrated Applied AI Experience<ul><li>Shipped LLM systems in production: at least one real system with an LLM at its core — an agent, copilot, RAG application, or AI-driven automation — used by real users, not a proof of concept.</li><li>Hands-on AI engineering: practical fluency with prompt and context engineering, tool/function calling, structured outputs, and agent frameworks or orchestration patterns.</li><li>Evaluation mindset: experience measuring and improving AI output quality — evals, test sets, feedback loops — and an honest understanding of failure modes (hallucination, drift, prompt injection).</li><li>Solid software engineering foundation: proficiency in Python, TypeScript, or Clojure; strong API and integration skills; the discipline to ship maintainable systems, not notebooks.</li><li>AI product sense: the judgment to identify which problems deserve an LLM, which need deterministic automation, and which should not be automated at all.</li></ul>Nice to Have<ul><li>Experience with workflow automation platforms (n8n, Zapier, or custom orchestration engines).</li><li>Exposure to cloud services (AWS) and infrastructure-as-code.</li><li>Familiarity with AI developer tooling (Claude Code, Cursor, Copilot) and AI governance practices.</li></ul>Behavioral &amp; Strategic Skills<ul><li>Builder bias: you prototype fast, validate with real users, and harden what works.</li><li>Governance-aware: you understand that "efficiency" must be balanced with "security," and you can design AI systems that are safe by default without destroying velocity.</li><li>Multiplier: you enjoy documenting your work, creating Golden Paths, and teaching others how to use what you build.</li><li>Comfortable with ambiguity: AI capabilities shift monthly; you treat that as an opportunity to re-solve problems better, not as churn.</li></ul><h2>Our Benefits</h2><ul><li>Chance of earning equity at Nubank</li><li>Food/ Meal Card (Vale-Refeição and/or Vale Alimentação)</li><li>Public Transportation Commuting Benefit (Vale-Transporte)</li><li>NuCare – Psychological, Financial and Legal Assistance Program</li><li>Life Insurance</li><li>Medical Plan</li><li>Dental Plan</li><li>NuLanguage – Language Course Program</li><li>Nucleo - Our learning platform of courses</li><li>Extended Parental Leave</li><li>Daycare Allowance</li><li>Parental Consultancy</li><li>Work-from-home Allowance</li><li>Gym Partnerships</li><li>30 days of paid vacation</li><li>Relocation Assistance Package, if applicable</li></ul><div> </div><h3>Work Model for this Role 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 <a target="_blank" rel="noopener noreferrer ugc" href="https://building.nubank.com/nu-hybrid-work-model/">https://building.nubank.com/nu-hybrid-work-model/ </a></h3><div> </div>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.

Nubank

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Staff Machine Learning Engineer

Lead Machine Learning Engineer / Applied AI Scientist

AI Research Manager - Machine Learning

Lead AI Engineer — Productivity Systems

Lead AI Engineer — Productivity Systems

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