What you'll be doing

• Contribute to the next generation of structured data foundation models.
• Collaborate with researchers and engineers to enhance 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 knowledge in ML and AI to tackle real challenges while contributing to scalable and adaptable solutions.
• Work across the full lifecycle of modern ML systems, including architecture design, training, post-training optimization, and inference acceleration.

Requirements

• 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.

Nice to haves

• 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.