Senior AI Researcher – Relational Foundation Models

What you'll do

• Design model architectures, pretraining strategies, learning objectives, and inference methods for relational and structured data; run distributed GPU experiments and develop evaluation benchmarks.
• Contribute datasets, synthetic data generators, and evaluation frameworks, then translate research findings into scalable prototypes for engineering and product teams.
• Provide technical leadership and mentorship, and publish research at top-tier venues.

What they ask for

• PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, or a closely related field — or equivalent demonstrated research experience.
• Strong publication record at leading venues such as NeurIPS, ICML, ICLR, KDD, AAAI, or AISTATS.
• Deep expertise in foundation-model techniques, including Transformer architectures, attention mechanisms, large-scale pretraining, in-context learning, and transfer learning.
• Hands-on experience in tabular machine learning, graph neural networks, relational learning, or structured prediction; excellent Python and PyTorch skills.

Nice to have

• Knowledge of relational database systems, SQL, or data-warehouse architectures.
• Experience with distributed training and large-scale GPU experimentation, benchmark development, or open-source machine learning libraries.

Tools

Python, PyTorch, Transformer architectures, GPU

AI focus

Research and develop relational foundation models for structured, multi-table enterprise data, including model architectures, pretraining, inference, datasets, and evaluation.