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About This Role
About Nu
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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 Institutional Page
About the Role
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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.
### Key Responsibilities
Applied AI \& Agentic Systems
- Design, build, and ship LLM\-powered agents and workflows that automate complex internal processes end\-to\-end.
- Work hands\-on with frontier models and the modern AI stack: tool/function calling, structured outputs, MCP, RAG, multi\-agent orchestration.
- Own the full lifecycle of an AI system: from problem discovery and prototype to production hardening, monitoring, and iteration.
Evaluation \& Reliability
- Build evaluation harnesses, guardrails, and quality feedback loops so AI systems can be trusted in production — not just demoed.
- Define what "good" looks like for non\-deterministic systems and instrument it: evals, regression suites, human\-in\-the\-loop review where it matters.
Intelligent Workflow Automation
- Use orchestration platforms (e.g., n8n) and custom integrations as delivery vehicles for AI\-in\-the\-loop automation across business units.
- Integrate enterprise platforms (Slack, Google Workspace, Jira, internal APIs) into coherent, AI\-assisted workflows.
AI Adoption \& Governance
- Drive the technical strategy for AI adoption within engineering and business workflows.
- Develop governance frameworks that make AI coding assistants and agents safe, compliant, and effective — balancing developer freedom with security and operational risk.
Multiplier Work
- Create Golden Paths, reference implementations, and documentation that let other teams build AI workflows safely on their own.
- 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.
- For Senior/IC5: execute complex AI projects with high autonomy, identify workflow bottlenecks worth automating, and mentor mid\-level engineers.
### What are we looking for?
Must Have — Demonstrated Applied AI Experience
- 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.
- Hands\-on AI engineering: practical fluency with prompt and context engineering, tool/function calling, structured outputs, and agent frameworks or orchestration patterns.
- 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).
- Solid software engineering foundation: proficiency in Python, TypeScript, or Clojure; strong API and integration skills; the discipline to ship maintainable systems, not notebooks.
- AI product sense: the judgment to identify which problems deserve an LLM, which need deterministic automation, and which should not be automated at all.
Nice to Have
- Experience with workflow automation platforms (n8n, Zapier, or custom orchestration engines).
- Exposure to cloud services (AWS) and infrastructure\-as\-code.
- Familiarity with AI developer tooling (Claude Code, Cursor, Copilot) and AI governance practices.
Behavioral \& Strategic Skills
- Builder bias: you prototype fast, validate with real users, and harden what works.
- 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.
- Multiplier: you enjoy documenting your work, creating Golden Paths, and teaching others how to use what you build.
- Comfortable with ambiguity: AI capabilities shift monthly; you treat that as an opportunity to re\-solve problems better, not as churn.
Location
- Miami, United States
- Palo Alto, United States
- Washington DC, United States
Our Benefits
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- 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
- 401K
- Saving Plans \- Health Saving Account and Flexible Spending Account
- Work\-from\-home Allowance
- Relocation Assistance Package, if applicable.
Work Model for this Role
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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/
Location\-Specific Disclosures
- Palo Alto: Total compensation includes base salary, RSUs and benefits. Base salary range: $11,712 \- $17,568
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.
Salary Context
This $11K-$17K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Nubank, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($14K) sits 93% below the category median. Disclosed range: $11K to $17K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Nubank AI Hiring
Nubank has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Miami, FL, US, Palo Alto, CA, US. Compensation range: $17K - $345K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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