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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.
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About the team
This role sits within Global Platforms Engineering: Engineering Enablement, under Eric Young (CTO, Global Platforms Engineering). The AI Developer Experience (AIDE) pillar is the AI\-native counterpart to the separately led Developer Infrastructure pillar: a standing topic in CTO org\-design reviews.
Reports to a Senior Director, Engineering Enablement and dotted line to Rahul Ahlawat, Engineering Senior Director \& Chief of Staff to the CTO. Leads a team of approximately 10–15 engineers today, spanning five workstreams, scaling with 8 committed "Deep Forge" headcounts plus external hiring. This is green\-field scope with established backing: the charter, budget conversations, and headcount pipeline already exist. The Director shapes execution and strategy, not just strategy alone, and has real decision\-making authority over team structure, build\-vs\-buy calls, and workstream prioritization. The Director will also engage directly with third\-party AI vendors (e.g., Factory.ai, Devin\-class tools) and with ICs across the five core workstreams.
About the role
Nubank is one of the few companies with the scale (3,500\+ engineers), AI ambition, and executive air cover to build a genuinely AI\-native developer experience: not a bolt\-on pilot. This role is responsible for defining and leading the AI Developer Experience pillar within Engineering Enablement, transforming how Nubank's engineers write, review, deploy, and operate software.
The Director inherits a mandate, a growing team, and a seat at the table with the CTO. The pillar already has real momentum: an early production win in the Vector agentic coding platform, an urgent context\-infrastructure buildout, and direct executive sponsorship.
Key responsibilities and Expectations
Own and scale the AI Developer Experience pillar, with an initial focus on measurably increasing shipped PRs per engineer per week through automation, and improving token efficiency as AI spend scales.
Build the team from the ground up, converting today's borrowed, part\-time contributors into a dedicated, full\-time organization; the CTO has personally committed to reallocating direct reports and unlocking headcount.
Run the build\-vs\-partner motion: stand up fast, structured engagements with third parties to benchmark, accelerate, and in some cases co\-build: while defining objective eval criteria so decisions aren't subjective.
Core workstreams:
- NuContext: the knowledge and context\-extraction layer (docs, Confluence, Jira, code) that everything else depends on; currently the single highest\-priority area for Nubank's long\-term AI strategy. Owns ontology definition, context curation standards, knowledge graph traversal/retrieval, and context update mechanisms.
- Vector (agentic coding \& hosted environment): sandboxing, harness, memory/retrieval, and system\-prompt optimization for AI\-assisted coding.
- Agentic Code Review: AI\-assisted PR review to cut defect rates and cycle time, a direct lever on the org's top\-line change failure rate (CFR) goal.
- AI SRE: moving from vendor evaluation to AI\-native incident detection, diagnosis, and remediation at scale.
- Prompt Classification \& Model Routing and 3rd\-party AI tooling vendor management: scope and placement still being finalized; the Director will help shape this boundary.
- Longer term, the AI Enablement Platform (routing, marketplace, authentication) is expected to fold into this scope as leadership bandwidth allows.
Qualifications
Professional Experience \& Education:
- Proven leadership of AI\-native developer tooling or platform teams, ideally at a company operating at meaningful engineering scale.
- Track record building full\-time, dedicated engineering teams out of fragmented, part\-time, or borrowed resourcing: including experience in allocating teams, stellar in people leadership to do it without burning teams out.
- Real experience structuring and running third\-party vendor partnerships (evaluation, integration, managing lock\-in risk) alongside first\-party build.
- Hands\-on technical credibility in agentic coding systems, context/retrieval infrastructure, or developer\-facing AI tooling: able to go deep with ICs, not just manage from a distance.
- Comfort operating in a high\-velocity, ambiguous, and high\-executive\-visibility organization; ability to align and mobilize teams to take on new challenges and scale impact.
- High accountability and ownership mindset, coupled with the ability to move fast under pressure.
- Comfort with an honest, unsolved problem space: even leading peer companies are far from 100% AI\-authored code; this role is about the challenge and the resourcing to attack it, not a pre\-solved problem.
- A track record that positions the Director as the natural next\-step candidate for the broader Engineering Enablement M6 role.
- A builder's instinct for evaluation rigor: experience designing evals/benchmarks that replace subjective quality judgments with objective, repeatable measurement.
- Experience with sandboxed agentic execution environments, retrieval\-augmented systems, or LLM\-based code review tooling (nice\-to\-have).
- Familiarity with token\-cost management as AI spend scales (nice\-to\-have).
- Prior exposure to fintech, regulated environments, or large legacy codebases where AI adoption is harder to get right (nice\-to\-have).
Core Leadership Skills
*The following executive leadership skills serve as a reference for senior leadership levels at Nubank, setting expectations that grow with increasing complexity, ambiguity, and influence.*
BUSINESS ACUMEN
Drives strategic business alignment at Nubank by analyzing market dynamics, identifying opportunities, and ensuring informed decision\-making to influence organizational direction.
COMMUNICATION
Masters complex communication, mentoring others, fostering open dialogue, and adapting strategies to enhance organizational effectiveness.
AGILE ADAPTABILITY
Leads with agility, pioneering solutions, fostering a culture of learning, collaboration, and continuous value delivery.
Role Location
Palo Alto, US: adhering to company policy for in\-office attendance (2026: 2x/week; 2027 onwards: 3x/week).
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.
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 in Demand for This Role
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. Director-level AI roles across all categories have a median of $274,554.
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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