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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About the role

As AI Research Manager, you will own the operating health and execution of Nu'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 Nu will depend on. Current and upcoming bets include:

Next-generation nuFormer architectures: proprietary transformers that learn from raw transaction sequences and power key production credit decisions

Multitask and multi-target modeling: single models serving many high-impact prediction tasks at once

Training and inference efficiency: distillation, quantization, sparsity, and parallelism to run state-of-the-art models economically at our scale

Causal modeling and policy optimization: moving beyond prediction to the decisions and policies those predictions should drive

World models: open-ended models that reason about a customer's full financial life

Recommendation systems: extending the backbone to app events, engagement, and personalization signals

Embeddings and representation learning: semantic IDs, contrastive learning, and reusable representations used across the bank

Real-time and continual learning: low-latency inference and models that adapt over time

Your job is to make exceptional science happen: build the conditions, focus, and operating rhythm that researchers do great work.

Location: Palo Alto, US

You'll be responsible for

People & Team Leadership

Lead, mentor, and advocate for a team of world-class ML researchers, fostering an environment of psychological safety, high ambition, and rigorous scientific inquiry.

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.

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.

Research Operations & Execution

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.

Allocate scarce, high-value resources, most notably GPU capacity, across competing research priorities, balancing exploration against the bets most likely to compound.

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.

Raise the bar on research rigor and communication: strong experimental design, peer review, and reproducibility.

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.

Strategy & Cross-Team Collaboration

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.

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.