Own and evolve the research roadmap for foundation models focused on relational data, knowledge graphs, representation learning, self-supervised approaches
Define scientific strategy and research priorities
Lead, mentor, and grow a team of research scientists and engineers
Establish a rigorous research process: hypothesis definition, RFCs, experimentation, evaluation, documentation
Design and implement state-of-the-art Graph Neural Networks for large-scale relational datasets
Solve node-, edge-, and graph-level learning problems, including multi-scale embeddings and temporal generalization
Build reliable training and evaluation pipelines using Python, PyTorch, PyTorch Geometric, distributed training
Define and maintain high-quality benchmarks and evaluation methodologies
Partner with product engineering and MLOps teams to transition research models into production
Collaborate with commercial and go-to-market teams
Account for production challenges: concept drift, model reliability, scalability, deployment
Provide technical direction while remaining hands-on
Requirements
7+ years of professional experience in AI/ML, or a PhD with at least 4 years of relevant experience
Demonstrated ability to take research concepts through to scalable, reliable production systems
Advanced hands-on proficiency in Python and PyTorch
Deep experience with graph learning frameworks such as PyTorch Geometric or DGL