Deep learning transformed text and images but mostly skipped tables, even though they're 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. We're building the foundation-model approach for tabular data.
We started with TabPFN. v2 was published in Nature and set a new state of the art on tabular benchmarks; since release we've scaled capabilities ~20x and crossed 3M+ downloads and 6k+ GitHub stars. The hard problems are still open: scaling to millions of rows, low-latency inference, new data modalities, and the infrastructure to run all of it in production.
Open roles:
- 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. Train your own architecture if you've got one.
- ML Engineer, Cloud Platform - design and scale the backend and infra that serve and finetune the models. Python/FastAPI, Terraform, K8s.
- Full Stack Engineer, ML Platform - build the product end to end, data upload through inference. TS + Python, React/FastAPI/Postgres.
- 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.
Also hiring: Applied Scientist, Forward Deployed ML Engineer, Developer Relations Engineer, AE, BDR.
30-person team with backgrounds from Google, G-Research, Jane Street, Goldman, CERN. Led by Frank Hutter, advised by Bernhard Schölkopf and Yann LeCun. Comp competitive with top AI labs.
Deep learning transformed text and images but mostly skipped tables, even though they're 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. We're building the foundation-model approach for tabular data. We started with TabPFN. v2 was published in Nature and set a new state of the art on tabular benchmarks; since release we've scaled capabilities ~20x and crossed 3M+ downloads and 6k+ GitHub stars. The hard problems are still open: scaling to millions of rows, low-latency inference, new data modalities, and the infrastructure to run all of it in production.
Open roles: - 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. Train your own architecture if you've got one. - ML Engineer, Cloud Platform - design and scale the backend and infra that serve and finetune the models. Python/FastAPI, Terraform, K8s. - Full Stack Engineer, ML Platform - build the product end to end, data upload through inference. TS + Python, React/FastAPI/Postgres. - 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.
Also hiring: Applied Scientist, Forward Deployed ML Engineer, Developer Relations Engineer, AE, BDR.
30-person team with backgrounds from Google, G-Research, Jane Street, Goldman, CERN. Led by Frank Hutter, advised by Bernhard Schölkopf and Yann LeCun. Comp competitive with top AI labs.
All roles: https://priorlabs.ai/careers