Multiple Roles

Deep learning transformed text and images but mostly skipped tables - 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.

Your whole dataset goes in as context, predictions come out in a single forward pass - no per-dataset training, no hyperparameter tuning, seconds instead of hours. It works: 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, production use from liquid biopsy to rail maintenance. As of last month we're an independent lab inside SAP, backed by €1B+ - models stay open, research stays public, same team and offices.

Open roles (most can sit in any of our 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/year on compute; you own that budget.

Research Scientist, Foundation Model - drive the model agenda: novel architectures, scaling 10K to 1M+ samples, multimodal and causal directions. PhD + top-venue publications or equivalent.

Research Engineer, Foundation Model - same agenda from the engineering side: you design experiments, write the training and eval infra, and co-author the papers. ML Engineer, Cloud Platform - design and scale the backend that serves and finetunes the models. Python/FastAPI, Terraform, K8s.

Full Stack Engineer, ML Platform - build the product end to end. TS + Python, React/FastAPI/Postgres. Also hiring: Applied Scientist, Forward Deployed ML Engineer, Research Scientist (Foundational Data Science), PhD research interns, plus GTM and ops roles. ~40-person team with background