Deep learning
Prior Labs | Berlin, Freiburg, NYC | ONSITE | Full-time | ML Infra, Research Scientist/Engineer, Backend, Full Stack We build foundation models for tabular data. Deep learning transformed text and images but mostly skipped tables, which are still the data behind most clinical trials, financial models and scientific experiments. The reason is structural: no natural sequence, no spatial structure, no shared vocabulary across datasets, so the architectures and scaling laws behind LLMs don't transfer. Our approach: pre-train a transformer on millions of synthetic datasets sampled from causal-structure priors. The whole dataset goes in as context, predictions come out in one forward pass. No per-dataset training, no hyperparameter tuning, seconds instead of hours. TabPFN v2 was published in Nature and set a new state of the art; TabPFN-3 scales to 10M rows. 4M+ downloads, 8k+ GitHub stars, in production from liquid biopsy to rail maintenance. Code: https://github.com/PriorLabs/TabPFN Since July we're an independent lab inside SAP, with more than EUR 1B committed over four years. Models stay open, research stays public, same team and offices. Roles (most can sit in any of the three offices): - Senior ML Infrastructure Engineer: own multi-cluster GPU infra (Slurm on GCP today, multi-provider next), training performance and the tooling layer. We spend tens of millions per year on compute; you own that budget. - Research Scientist, Foundation Model: drive the model agenda - novel architectures, scaling from 10K to 1M+ samples, multimodal and causal directions. PhD plus top-venue pub