About The Position
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. You will partner with world-class researchers and engineers across the full machine learning lifecycle, from architecture exploration and large-scale training to post-training optimization and high-performance inference. This is an opportunity to turn foundational research into production systems that influence how organizations derive intelligence from complex, connected data. NVIDIA foundation models are optimized for NVIDIA-accelerated infrastructure, providing a strong platform for translating advanced AI research into deployable systems. If you are energized by graph learning, relational reasoning, and building AI that moves beyond single-table benchmarks, we would love to hear from you.
Requirements
- 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
Nice To Haves
- Familiarity with graph-based machine learning; publications at venues such as NeurIPS, ICLR, ICML, or similar
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.
Benefits
- equity
- benefits
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What This Job Offers
Job Type
Full-time
Career Level
Senior
