Relational Foundation Model Engineer, Modern Data Stack

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Описание

Our work at NVIDIA is dedicated towards a computing model focused on visual and AI computing. For two decades, NVIDIA has pioneered visual computing, the art and science of computer graphics, with our invention of the GPU. The GPU has also shown to be spectacularly effective at solving some of the most complex problems in computer science. Today, NVIDIA’s GPU simulates human intelligence, running deep learning algorithms and acting as the brain of computers, robots and self-driving cars that can perceive and understand the world. We are looking to grow our company and teams with the smartest people in the world and there has never been a more exciting time to join us! NVIDIA is redefining what’s possible with AI, and our Relational Foundation Model team is at the forefront of that mission. We’re building a single, unified foundation model that understands the structure and relationships within any relational database or heterogeneous graph — a fundamentally new approach to enterprise AI. As an engineer on this team, you won’t just be fine-tuning existing models; you’ll be designing and experimenting with novel Transformer and GNN architectures that generalize across diverse relational schemas. Your work will directly impact real-world applications spanning recommendation systems, demand forecasting, fraud detection, and predictive maintenance — all powered by one extensible model. You’ll collaborate closely with researchers and engineers across the full ML lifecycle, from architecture exploration and large-scale training to post-training optimization and inference acceleration. This is a rare opportunity to contribute to foundational research that ships into production and shapes the modern data stack. If you’re excited about graph learning, relational reasoning, and building AI systems that go far beyond single-table benchmarks, this is the team for you. What you’ll be doing: Collaborate with researchers/engineers to enhance our Transformer and GNN-based models to

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