Head of Research (AI/ML)

Location: Remote, São Paulo, Brazil

Company

Building relational foundation models for enterprise decision-making, applying graph-native models to credit, fraud, growth, monitoring, and other high-stakes prediction problems.

What you will do

• Own the research roadmap for foundation models focused on knowledge graphs, representation learning, and self-supervised and unsupervised methods.
• Lead, mentor, and grow a team of research scientists and engineers.
• Design and implement state-of-the-art graph neural networks for node-, edge-, and graph-level tasks, including multi-scale embeddings and temporal or inductive generalization.
• Build reproducible training, evaluation, and distributed inference pipelines with PyTorch and PyTorch Geometric.
• Define rigorous benchmarks and research processes covering hypotheses, RFCs, experimentation, and statistically sound model comparisons.
• Partner with Product Engineering, MLOps, Data, and GTM teams to productionize models, define enterprise success criteria, and communicate business impact.

Requirements

• 7+ years of AI/ML experience, or a PhD plus 4 years of experience.
• Experience shipping research from papers or prototypes into scalable, reliable production systems with measurable business impact.
• Hands-on expertise in Python and PyTorch, with deep proficiency in graph learning libraries such as PyTorch Geometric or DGL.
• Experience with graph-based models, distributed training and inference, self-supervised or contrastive learning, and software engineering practices including testing and profiling.
• Experience mentoring or leading technical projects or managing a team of 2–6 scientists or engineers.
• Professional proficiency in both Portuguese and English.

Nice to have

• Strong publication record in NeurIPS, ICML, ICLR, or other top-tier AI conferences.
• Significant open-source contributions.

Culture & Benefits

• Research is expected to translate into useful, production-grade capabilities.
• Close collaboration with internationally recognized research advisors.
• Work across research, data, product engineering, MLOps, and go-to-market functions.