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.

All roles: https://jobs.priorlabs.ai

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.

Apply at: https://jobs.priorlabs.ai

Comments URL: https://news.ycombinator.com/item?id=45952746

Points: 7

# Comments: 0

Prior Labs is building foundation models for structured/tabular data – AI's biggest blind spot. While LLMs handle text/images, tables (numbers, categories, text mix) need native understanding. Our approach, TabPFN (published in Nature, 1M+ downloads, 3k+ GitHub stars), uses transformers pre-trained on synthetic data to achieve state-of-the-art results on small datasets zero-shot.

We're tackling a $100B+ opportunity to transform data science, finance, healthcare, and science. Backed by €9M pre-seed from Balderton, XTX Ventures, and leaders from Hugging Face, DeepMind, etc.

We're hiring a founding team to build this new category:

Software Engineer: Build scalable infra & APIs. Data Scientist / ML Engineer: Optimize & scale our TFMs. Product Manager: Define the vision for AI-native tabular tools. Developer Relations: Grow our community & drive adoption. Location: On-site in Berlin or Freiburg (we build together). Offer: Competitive salary + significant equity. Shape the future of AI for structured data from day one.

Apply: https://jobs.ashbyhq.com/prior-labs

Questions? Reach out at noah@priorlabs.ai

Comments URL: https://news.ycombinator.com/item?id=43783345

Points: 2

# Comments: 0

Prior Labs is building foundation models for structured/tabular data – AI's biggest blind spot. While LLMs handle text/images, tables (numbers, categories, text mix) need native understanding. Our approach, TabPFN (published in Nature, 1M+ downloads, 3k+ GitHub stars), uses transformers pre-trained on synthetic data to achieve state-of-the-art results on small datasets zero-shot.

We're tackling a $100B+ opportunity to transform data science, finance, healthcare, and science. Backed by €9M pre-seed from Balderton, XTX Ventures, and leaders from Hugging Face, DeepMind, etc.

We're hiring a founding team to build this new category:

Software Engineer: Build scalable infra & APIs. Data Scientist / ML Engineer: Optimize & scale our TFMs. Product Manager: Define the vision for AI-native tabular tools. Developer Relations: Grow our community & drive adoption. Location: On-site in Berlin or Freiburg (we build together). Offer: Competitive salary + significant equity. Shape the future of AI for structured data from day one.

Apply: https://jobs.ashbyhq.com/prior-labs

Questions? Reach out at noah@priorlabs.ai

AI has transformed text, images, and code—but structured data remains overlooked. Prior Labs is an early-stage startup building Foundation Models for tabular data, unlocking a new AI modality with the potential to transform science, finance, healthcare, and data science itself. Our model, published in Nature, is already state-of-the-art for small datasets, and we’re scaling this into a step-change for data science.

We’re backed by Balderton, XTX Ventures, and leaders from Hugging Face, Black Forest Labs, DeepMind, DataRobot, .. Our team includes world-class researchers and engineers from top AI labs, and we’re growing fast.

We're hiring founding engineers & builders to help define this new category:

Software Engineer – Build scalable infrastructure and APIs to integrate our models into real-world applications.

Product Manager – Define and execute the vision for AI-native tools for structured data.

Developer Relations – Grow the developer community, drive adoption, and showcase use cases.

ML Engineer – Optimize and scale our foundation models for structured data.

Location: On-site in Berlin or Freiburg (we believe in building together). Why Join? Competitive salary + equity, shape the future of foundation models for structured data from day one.

Apply now: https://jobs.ashbyhq.com/prior-labs

Questions? Reach out at noah@priorlabs.ai

There's some interesting work on using LLMs for tabular data (TabLLM: https://proceedings.mlr.press/v206/hegselmann23a.html), but this only works for datasets with tens of samples rather than the thousands of rows needed in real-world applications.

What o1 and other LLMs typically do is wrap around existing tabular tools like XGBoost or scikit-learn. While this works, they're ultimately constrained by these tools' limitations. We're taking a fundamentally different approach - building foundation models that natively understand tabular relationships and patterns. Our approach combines the benefits of foundation models with architectures specifically designed for tabular data structures.

  TabPFN mean ROC AUC: 0.9973

  SVM mean ROC AUC: 0.9903

  TabPFN per split: [0.99737963 0.99639699 0.99966931 0.99338624 0.99966465]

  SVM per split: [0.99312152 0.98788077 0.99603175 0.98313492 0.99128102]

  from sklearn.model_selection import cross_val_score
  from tabpfn import TabPFNClassifier
  from sklearn.datasets import load_breast_cancer
  from sklearn.svm import LinearSVC
  import numpy as np

  data = load_breast_cancer()
  X, y = data.data, data.target

  # TabPFN
  tabpfn_clf = TabPFNClassifier()
  tabpfn_scores = cross_val_score(tabpfn_clf, X, y, cv=5, 
  scoring='roc_auc')
  print("TabPFN per split:", tabpfn_scores)
  print("TabPFN mean ROC AUC:", np.mean(tabpfn_scores))
  
  # SVM
  svm_clf = LinearSVC(C=0.01)
  svm_scores = cross_val_score(svm_clf, X, y, cv=5, 
  scoring='roc_auc')
  print("SVM per split:", svm_scores)
  print("SVM mean ROC AUC:", np.mean(svm_scores))