About the job
NVIDIA is redefining what is possible with AI, and the Relational Foundation Model team is helping lead that transformation. We are building a unified foundation model that can understand the structure, context, and relationships within relational databases and heterogeneous graphs—opening a new frontier for enterprise AI. As an engineer on this team, you will go beyond adapting existing models: you will design, build, and evaluate novel Transformer and graph neural network architectures that generalize across diverse data schemas. Your work will power meaningful applications, including recommendation systems, demand forecasting, fraud detection, and predictive maintenance.
Responsibilities
Collaborate with researchers/engineers to enhance our Transformer and GNN-based models to operate seamlessly over any relational schema and heterogeneous graph.
Gain hands-on experience with high-impact use cases such as forecasting, entity matching, customer retention and fraud detection – all built on top of a single, extensible foundation model.
Leverage your knowledge in ML and AI to tackle real challenges while contributing to scalable and adaptable solutions that push the boundaries of what’s possible.
Work may span the full lifecycle of modern ML systems: from architecture design/training to post-training optimization and inference acceleration.
Contribute to our next generation of the Relational Foundation Model.
Qualifications
Minimum
MS or PhD in Machine Learning, Computer Science, or equivalent experience
Proficiency in Python and deep learning frameworks, such as PyTorch
At least 8 years of research experience in designing ML algorithm solutions
Practical experience in using Predictive Models in Real World Applications
Preferred
Familiarity with graph-based machine learning; publications at venues such as NeurIPS, ICLR, ICML, or similar