About the role
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 Structured Data Foundation Model team is at the forefront of that mission. We’re building unified foundation models that understand structured data in its many forms: relational databases and heterogeneous graphs, tabular data, and time series. As an engineer on this team, you’ll be designing and experimenting with novel architectures (Transformers, GNNs, and beyond) that generalize across diverse schemas, domains, and data modalities. Your work will directly impact real-world applications spanning recommendation systems, demand forecasting, fraud detection, and predictive maintenance. If you’re excited about relational learning, tabular modeling, time series forecasting, and building AI systems that go far beyond single-table benchmarks, this is the team for you.
What you’ll be doing:
You will contribute to our next generation of structured data foundation models.
Collaborate with researchers/engineers to enhance our foundation models to operate over relational schemas, heterogeneous graphs, tabular data, and time series.
Design and run benchmarks that evaluate generalization across structured data domains, and use the results to drive model improvements.
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.
What we need to see:
MS or PhD in Machine Learning, Computer Science, or equivalent program
Proficiency in Python and PyTorch
At least 5 years of research experience in designing ML algorithm solutions
Practical experience in using Predictive Models in Real World Applications
Ways to stand out from the crowd:
Experience with time series forecasting, tabular, or relational foundation models (e.g., Chronos, TimesFM, TabICL, TabPFN, or similar), including pretraining, fine-tuning, or benchmarking
Familiarity with graph-based machine learning; publications at venues such as NeurIPS, ICLR, ICML, KDD, or similar
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you!
Originally posted on Himalayas