Description
NVIDIA's Structured Data Foundation Model team is seeking an engineer to design and experiment with novel architectures that generalize across diverse schemas, domains, and data modalities. You will contribute to unified foundation models that understand structured data in various forms, including relational databases, heterogeneous graphs, tabular data, and time series.
Responsibilities:
- Contribute to the next generation of structured data foundation models.
- Collaborate with researchers and engineers to enhance foundation models for relational schemas, heterogeneous graphs, tabular data, and time series.
- Design and run benchmarks to evaluate generalization across structured data domains and drive model improvements.
- Leverage machine learning and AI expertise to tackle real challenges and develop scalable solutions.
- Work across the full lifecycle of modern ML systems, from architecture design to post-training optimization and inference acceleration.
Requirements:
- Master's or PhD in Machine Learning, Computer Science, or equivalent.
- Proficiency in Python and PyTorch.
- At least 5 years of research experience in designing ML algorithm solutions.
- Practical experience using predictive models in real-world applications.
Preferred Qualifications:
- Experience with time series forecasting, tabular, or relational foundation models.
- Familiarity with graph-based machine learning and publications at top venues like NeurIPS, ICLR, ICML, KDD, or similar.