ML Engineer
Posted 25 Jun 2026
Advertised as “ML Engineer, Forward Deployed”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 3+ yrs
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
No salary listed for this role.
Who we are
Foundation models transformed text and images. Structured data - the largest and most consequential data format in the world - stayed untouched, until now. What LLMs did for language, we're doing for tables.
We pioneered tabular foundation models: TabPFN v2 was a Nature cover story, has passed 3.5M+ downloads and 7,500+ GitHub stars, and runs in production from detecting lung disease with Oxford Cancer Analytics to preventing train failures with Hitachi. The hardest problems - millions of rows, real-time inference, entirely new modalities - are still open, and no one else is working on them at this level.
We're a small, highly selective team of 40+ with backgrounds from Google, DeepMind, Meta, Apple, Amazon, Jane Street, and CERN, led by Frank Hutter, Noah Hollmann, and Sauraj Gambhir, and advised by Bernhard Schölkopf and Turing Award winner Yann LeCun.
In July 2026, less than 18 months after our €9M pre-seed, we joined SAP as an independent frontier AI lab - same team, mission, and open-weights models, now backed by more than €1 billion over four years.
Core Areas of Impact
A model that tops a benchmark and a model that changes how an organization works are two different things. You'll take our tabular foundation models into our most strategic customers' environments - integrating them into real platforms and pipelines, doing the hands-on data science to prove value, and owning the implementation through to production.
This is a senior role: You'll own engagements end-to-end with high autonomy, make hard technical calls under ambiguity, and work shoulder-to-shoulder with customer data science teams as a senior peer. The patterns you uncover in the field won't stay in the field - they flow straight back to our researchers and shape what we build next. You won't just deploy models; you'll help shape how Prior Labs deploys as we scale.