Member of Technical Staff, Research — Relational Foundation Models

Avra is building a foundation model for relational data. The role focuses on researching and developing models that generalize across unfamiliar databases, including pretraining, in-context learning, data selection, and rigorous evaluation.

Responsibilities:

• In-context learning over relational data. How the model uses labelled examples at inference time, what makes a good context (which rows, how much history, which neighbours), and why it fails when it fails
• Pretraining from scratch. Architectures, objectives, and scaling for transformers over multi-table, temporal data, on multi-GPU H100 runs
• Pretraining data. Which corpora, real and synthetic, teach a model to generalise across schemas, domains, and languages
• Time and leakage. Predictions that only see what was knowable at decision time. That's the difference between a benchmark number and a model a bank can trust
• Evaluation. Relational and tabular benchmark suites, strong baselines (gradient boosting, tabular foundation models, task-specific relational models), and protocols that keep comparisons honest
• Beyond the context window. When fine-tuning or post-training on a customer's database beats in-context prediction, and by how much