Research Scientist, Foundational Data Science

The role

This role involves building the foundations of tabular foundation models. You will invent frontier tools for TabPFN and build the dataset and benchmark bedrock they stand on.

What you will do

• Invent and build frontier tools that extend TabPFN including thinking, scaling, and agentic capabilities.
• Set the research direction by deciding which model capabilities and benchmarks are worth pursuing.
• Bring in external research and real customer needs to shape new model and tooling directions.
• Publish frontier results that move the field forward.
• Build trustworthy benchmarks from structured data behind real, high-impact problems.
• Implement baselines and competitor models to compare against TabPFN.
• Build an automated, agentic pipeline with a human in the loop to scale data and benchmark foundation.

Required qualifications

• Solved data-science problems across many domains and datasets to a high standard.
• Work undogmatically across the ML toolbox, including gradient-boosted trees like XGBoost and deep learning.
• Understand common categories of dataset defects such as leakage, label noise, distribution shift, duplication, and mislabeled targets.
• Thrive as a senior individual contributor in an ambiguous, early-stage, low-process environment.
• Be opinionated on best practice in Data Science and make good judgement calls on complex problems.

Nice to have

• Experience building or extending evaluation harnesses, benchmark suites, or experiment frameworks.
• Experience building LLM- or agent-assisted pipelines with a human in the loop.
• Experience acting as the link between external research or customer needs and an internal model or product roadmap.
• Prior work on tabular, structured-data, or foundation-model problems.
• Helping shape an emerging research subfield through community work.

About the company

Prior-labs is an independent frontier AI lab backed by SAP. The team works on tabular foundation models.