avra

Head of Research, AI

avra

full-time

Posted on:

Origin:  • 🇧🇷 Brazil

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Job Level

Lead

Tech Stack

AWSNode.jsPythonPyTorch

About the role

  • Own the research roadmap for our Graph Foundation Models, focusing on knowledge graphs, representation learning, self-supervised and unsupervised methods
  • Lead, mentor, and grow a world-class team of research scientists and engineers
  • Establish and champion a rigorous research cadence: from hypothesis definition and RFCs to disciplined experimentation and clear, data-driven decision-making
  • Design and implement state-of-the-art GNNs for our unique, large-scale graph; solve complex problems in node, edge, and graph-level tasks, multi-scale embeddings, and temporal/inductive generalization
  • Build reliable, reproducible training and evaluation pipelines using PyTorch, PyG, and distributed training frameworks
  • Define and maintain our gold-standard benchmarks, ensuring statistically sound model comparisons
  • Collaborate with Product Engineering to productionize models to serve both batch and online inference to our customers
  • Partner with GTM teams to define success criteria for enterprise client bake-offs and communicate the impact of your team's work to technical and executive stakeholders
  • Ensure research accounts for real-world systems challenges, including concept drift, and improve strategy for model deployment in regulated contexts

Requirements

  • 7+ years of experience in AI/ML (or a PhD + 4 years)
  • Demonstrated ability to ship research into production (take ideas from a paper or prototype to scalable, reliable code that delivered measurable business impact)
  • Hands-on excellence in Python and PyTorch
  • Deep proficiency in graph learning libraries like Pytorch Geometric or DGL
  • Solid software engineering fundamentals, including testing, profiling, and building maintainable systems
  • Proven experience mentoring and leading technical projects or managing a small team (2-6) of scientists/engineers
  • Professional proficiency in both Portuguese and English
  • Preferred qualifications:
  • Experience with distributed training and inference
  • Experience working with graph-based models
  • Deep knowledge of self-supervised or contrastive learning techniques, particularly for graphs
  • Strong publication record in top-tier AI conferences (NeurIPS, ICML, ICLR) or significant open-source contributions