Technical Recruiter

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+ Git Hub 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, Deep Mind, 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.

We're hiring a technical recruiter to own hiring across Science, Engineering and Data Science. These are three distinct talent markets with three distinct assessment bars, and you'll own all of them end-to-end. We're scaling fast, and competing directly with the best-funded labs in the world for the same people.

• Source scarce ML research, infrastructure, and forward-deployed talent globally, exploring mainstream and niche talent pools
• Position Prior Labs compellingly against other foundation model labs such as OpenAI, Anthropic, Deep Mind, and Mistral and against the enterprise AI companies competing for FDE talent
• Navigate multi-hub hiring across Freiburg, Berlin, and New York (visas, comp bands, notice periods)
• Screen technical depth quickly across research, engineering, and customer-facing profiles without burning researcher or founder time
• Run efficient, structured interview loops with clear rubrics and fast debriefs, calibrated separately for research, engineering, and FDE archetypes
• Manage, negotiate and close offers, including against competing frontier-lab packages
• Manage the pipeline operationally in the ATS with clean stages and weekly metrics

• 5+ years in-house technical recruiting at a deep-tech, AI/ML, or research-led startup (Series A and ), or in a frontier lab's talent team
• Track record closing ML researchers or research engineers against top tier frontier labs (e.g. OpenAI, Anthropic, Deep Mind, Mistral)
• Experience hiring at least one customer-facing engineering profile (forward deployed, solutions, or applied engineering) — the assessment bar is different and you'll need to know why
• Hands-on hiring across at least two of:
  Germany, wider EU, and the US (NYC)
• Fluency in the ML talent landscape: top labs, conferences (NeurIPS, ICML, ICLR, AutoML), and what distinguishes a strong research vs. engineering vs. infrastructure profile
• Modern ATS architecture (Ashby or equivalent): stage design, rejection taxonomy, funnel reporting