This one is closed
Live roles like this one
- C 1w ago
See every "Berlin Freiburg NYC" role →
Get new “Berlin Freiburg NYC” roles by email
One email a day with what is new in "Berlin Freiburg NYC". Nothing new, no email.
We confirm the address first, and every mail carries an unsubscribe link. Alerts are ours, not a third party's.
Why this grade This listing scored 37/100, which is an F. It lost the most ground on pay transparency. See the breakdown
- Description depth 20 / 20 How much the posting actually says about the work, measured in characters of real text.
- Pay transparency 12 / 25 A published salary range, worth more than any other single factor because it is what a candidate cannot find out without applying.
- Remote clarity 8 / 15 Whether "remote" means anywhere, or is quietly restricted to one country.
- Corroboration 5 / 10 Whether more than one source carries this listing.
- Freshness 4 / 15 How recently it was posted. Older postings are likelier to be filled or abandoned.
- Role specificity 3 / 10 Whether the listing is tagged well enough to tell what the role actually is.
-15 Ghost-job penalty — Deducted for signals that this posting may not be a real, currently-open role — staleness, repeated relisting, or talent-pool language.
Every figure above is arithmetic over the posting itself — its salary field, its text, its age, its tags and how many sources carry it. How the grades work →
hn-hiring
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.
All roles: https://jobs.ashbyhq.com/prior-labs
Apply for this role Opens priorlabs.ai — the employer's own page, as linked by the source that listed this role
Quick question · anonymous · one tap
Would you apply to this job?
Answer to see what other job seekers said.
Your turn · no account needed
Help the next applicant
You may know something about this listing that we cannot see from here. One tap. No account needed. Signed-in reports earn points once the evidence agrees with you.
I know what it pays
Sign in with Google to earn points for reports — 100 confirmed points buy a week of Early Access.
Where this listing came from
- 04 Aug 2026 Hacker News first sighting
Seen on 1 board over 28 days. The employer edited the description 1× since we first recorded it.