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 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. TypeScript + 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 people with backgrounds from Google, DeepMind, Jane Street, Goldman, G-Research, 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 - 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 backgrounds from Google, DeepMind, Jane Street, Goldman, G-Research, 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 7.5k+ 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
- ML Engineer, Cloud Platform
- Full Stack Engineer, ML Platform
- Research Scientist, Foundation Model
- Applied Scientist
- Forward Deployed ML Engineer
- Developer Relations Engineer
- AE
35-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.
Rejection letter encourages to “keep an eye on our future opportunities as we continue to grow”. Tried applying – couldn’t because I’ve “applied in the past”.
Tables power every financial model, clinical trial, and scientific experiment, but deep learning has mostly ignored them. No natural sequence, no spatial structure, no shared vocabulary across datasets. LLM architectures don't transfer. We built TabPFN, the first foundation model that actually understands tabular data (published in Nature, 3M+ downloads, new SOTA for tabular ML). The hardest problems are still open.
The model is half the product. The other half - training infrastructure, real-time serving, developer platform, reliability - is what turns a research breakthrough into something enterprises trust in production. We're hiring across both.
ML Engineer, Cloud Platform — Design and scale the core infrastructure for serving and finetuning foundation models in production. Early enough that you're making the architecture decisions, not inheriting them.
ML Engineer, Training Infrastructure — Own GPU infrastructure, distributed training performance, and the developer productivity layer (CI, experiment tracking, model registry) that keeps research moving fast.
Full Stack Engineer, ML Platform — Build the product that puts tabular foundation models in users' hands, from data upload through inference and results. You'll work across frontend, backend, and directly with the research team to turn new model capabilities into production features.
Research Engineer, Foundation Model — Design experiments, run ablations, build training infrastructure, contribute to papers. Research engineers here aren't supporting scientists — they are the science team.
Also hiring: Research Scientist, Applied Scientist, Forward Deployed ML Engineer, Developer Relations Engineer, AE, BDR.
20-person team selected from thousands applicants. Backgrounds from Jane Street, Google, CERN, G-Research. Led by Frank Hutter, advised by Yann LeCun and Bernhard Schölkopf. €9M pre-seed from Balderton, with backing from leaders at Hugging Face, DeepMind, and Black Forest Labs. Comp competitive with top AI labs, meaningful equity.
Prior Labs | Full Stack Engineer, ML Platform Hiring: Berlin | Salary: Competitive with top AI labs + meaningful equity | Relocation support | Full Time
Building the product that puts tabular foundation models into users' hands. React, FastAPI, Postgres. You'll work across frontend, backend, and directly with researchers shipping new model capabilities to production. 3+ years full stack, strong TypeScript and Python, experience with data-intensive applications. https://jobs.priorlabs.ai
The application link is broken and IIRC that’s been the case for this job post each time it’s posted. Generic rejection letter also… IMO not worth applying.
Tables power every clinical trial, financial model, and scientific experiment, but deep learning has mostly ignored them. No natural sequence, no spatial structure, no shared vocabulary across datasets. LLM architectures don't transfer. We built TabPFN, the first foundation model that actually understands tabular data (published in Nature, 3M+ downloads, new SOTA for tabular ML). The hardest problems are still open.
The model is half the product. The other half - training infrastructure, real-time serving, developer platform, reliability - is what turns a research breakthrough into something enterprises trust in production. We're hiring across both.
- ML Engineer, Training Infrastructure — Own GPU infrastructure, distributed training performance, and the developer productivity layer (CI, experiment tracking, model registry) that keeps research moving fast.
- Full Stack Engineer, ML Platform — Build the product that puts tabular foundation models in users' hands, from data upload through inference and results. You'll work across frontend, backend, and directly with the research team to turn new model capabilities into production features.
- Research Engineer, Foundation Model — Design experiments, run ablations, build training infrastructure, contribute to papers. Research engineers here aren't supporting scientists — they are the science team.
- ML Engineer, Cloud Platform — Design and scale the core infrastructure for serving and finetuning foundation models in production. Early enough that you're making the architecture decisions, not inheriting them.
Also hiring: Research Scientist, Applied Scientist, Forward Deployed ML Engineer, Developer Relations Engineer, AE, BDR.
20-person team selected from thousands applicants. Backgrounds from Jane Street, Google, CERN, G-Research. Led by Frank Hutter, advised by Yann LeCun and Bernhard Schölkopf. With backing from leaders at Hugging Face, DeepMind, and Black Forest Labs, XTX Ventures & Balderton.
Tables power every clinical trial, financial model, and scientific experiment, but deep learning has mostly ignored them. No natural sequence, no spatial structure, no shared vocabulary across datasets. LLM architectures don't transfer. We built TabPFN, the first foundation model that actually understands tabular data (published in Nature, 3M+ downloads, new SOTA for tabular ML). The hardest problems are still open.
The model is half the product. The other half - training infrastructure, real-time serving, developer platform, reliability - is what turns a research breakthrough into something enterprises trust in production. We're hiring across both.
ML Engineer, Cloud Platform — Design and scale the core infrastructure for serving and finetuning foundation models in production. Early enough that you're making the architecture decisions, not inheriting them.
ML Engineer, Training Infrastructure — Own GPU infrastructure, distributed training performance, and the developer productivity layer (CI, experiment tracking, model registry) that keeps research moving fast.
Full Stack Engineer, ML Platform — Build the product that puts tabular foundation models in users' hands, from data upload through inference and results. You'll work across frontend, backend, and directly with the research team to turn new model capabilities into production features.
Research Engineer, Foundation Model — Design experiments, run ablations, build training infrastructure, contribute to papers. Research engineers here aren't supporting scientists — they are the science team.
Also hiring: Research Scientist, Applied Scientist, Forward Deployed ML Engineer, Developer Relations Engineer, AE, BDR.
20-person team selected from thousands applicants. Backgrounds from Jane Street, Google, CERN, G-Research. Led by Frank Hutter, advised by Yann LeCun and Bernhard Schölkopf. With backing from Balderton, XTX Ventures and leaders at Hugging Face, DeepMind, and Black Forest Labs. Comp competitive with top AI labs, meaningful equity.
Yes exactly, the API is the best way to handle text features. The actual semantics often matter a lot . Is the API an option for you or would you need this local?
Less feature engineering is definitely something we are aiming for. The current version is actually only based on statistics, the real world connections between features is something we're working on right now and hope to show results for soon. That's the next step
When we released TabPFNv1 over three years ago, I didn’t expect at all the hundreds of comments and reposts we would see. Tabular data had been a field getting little love from AI research—but we immediately felt that this was a topic that data scientists, scientists, financial analysts, and enterprise users deeply cared about. Glad its useful to people!
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 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. TypeScript + 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 people with backgrounds from Google, DeepMind, Jane Street, Goldman, G-Research, CERN. Led by Frank Hutter, advised by Bernhard Schölkopf and Yann LeCun. Comp competitive with top AI labs.
All roles and applications: https://jobs.ashbyhq.com/prior-labs
Questions welcome in the replies here.
reply