AI Research Manager - Machine Learning

Nubank•Palo Alto, CA

3d•Hybrid

About The Position

Nubank is seeking an AI Research Manager to lead the operating health and execution of its Machine Learning research agenda. This is a people-management role focused on a small, high-talent-density team of world-class researchers, working in close partnership with senior technical leads who guide the scientific direction. The team focuses on a portfolio of research bets with a one-to-four-quarter horizon, aiming to develop foundational capabilities that will provide a long-term advantage for Nubank. Current and upcoming research areas include next-generation nuFormer architectures, multitask and multi-target modeling, training and inference efficiency, causal modeling and policy optimization, world models, recommendation systems, and embeddings and representation learning. The primary goal of this role is to create the optimal environment for researchers to produce exceptional science.

Requirements

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.
M.S. or Ph.D. in Computer Science, Machine Learning, Applied Mathematics, or a related quantitative field.
Strong working understanding of core AI methodologies.
Proven ability to build, scale, and retain high-performing research or advanced applied-science teams.
Experience operating in global, distributed teams and navigating the path from foundational research to scalable, production-grade systems.

Nice To Haves

Deep conceptual familiarity with at least one of our core research areas: Foundation models & LLMs (pre-training from scratch, scaling laws, training-optimization frameworks, and large GPU-cluster workflows).
Behavioral & sequential models (sequence models, recommendation systems, and large-scale representation learning for very large user bases).
Decisioning & optimization (causal inference, policy optimization, constrained optimization, or reinforcement learning).
Training & inference efficiency (model sparsification, quantization, distillation, or parallelism and partitioning design).
Exceptional storytelling skills, with a track record of translating complex technical milestones into business impact for senior and C-level audiences.

Responsibilities

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.
Attract and retain top-tier research talent in a competitive market.
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 to enable rigorous, multi-quarter work.
Raise the bar on research rigor and communication, including experimental design, peer review, and reproducibility.
Cultivate the team's standing in the broader research community through publishing, open-source contributions, and conference presence.
Partner closely with senior technical leads who drive architecture and scientific direction, aligning operational execution with the long-term research roadmap.
Ensure clean handoffs from research into production, so breakthrough results land reproducibly in the hands of applied teams.
Translate complex, frontier technical progress into clear, high-impact narratives for executive leadership, connecting research milestones to business outcomes.
Bridge foundational research and long-term business strategy, ensuring the team's technical inputs directly enable Nubank's AI-first direction.