Account Executive, Berlin

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 https://www.nature.com/articles/s41586- cover story, has passed 3.5M+ downloads and 7,500+ GitHub stars, and runs in production from detecting lung disease with Oxford Cancer Analytics https://www.oxcan.org/news/prior-labs-and-oxford-cancer-analytics-partner-to-advance-liquid-biopsy-and-clinical-decision-making-in-lung-disease to preventing train failures with Hitachi https://siliconangle.com/2/prior-labs-debuts-tabular-ai-foundation-model-scales-10-million-rows/. 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+ https://priorlabs.ai/about with backgrounds from Google, DeepMind, Meta, Apple, Amazon, Jane Street, and CERN, led by Frank Hutter https://www.linkedin.com/in/frank-hutter-9190b24b/, Noah Hollmann https://www.linkedin.com/in/noah-hollmann-668b9010b/, and Sauraj Gambhir https://www.linkedin.com/in/sauraj-g/, 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 https://priorlabs.ai/blog-posts/priorlabs-sap as an independent frontier AI lab - same team, mission, and open-weights models, now backed by more than €1 billion over four years.

ABOUT THE ROLE

We are hiring our first Account Executive to drive commercial adoption of our foundation models for structured data. This is a founding role — you'll shape how we sell, who we sell to, and what good looks like for every AE who joins after you. You'll own the full sales cycle across enterprise and mid-market accounts, working directly with the commercial founder and a team of ex-DeepMind, Palantir, Jane Street, Goldman Sachs and Amazon researchers and operators.

This role combines a strong outbound focus with the potential for end-to-end deal ownership. You'll spend most of your time on outreach and discovery with technical buyers (data scientists, ML engineers, technical leaders), while also navigating buying committees, developing proposals, and closing deals. You'll help create the materials and messaging that make our outbound motion successful while driving revenue outcomes. We're looking for someone who loves the craft of outbound and wants to build the engine, not just close deals.

OUTBOUND AND PIPELINE GENERATION

• Drive outbound outreach to high-potential accounts through email, LinkedIn, and community channels
• Execute targeted campaigns and iterate based on what works
• Qualify inbound leads and identify opportunities worth pursuing

SALES CYCLE OWNERSHIP

• Run discovery calls and product demos with data science and ML teams
• Develop proposals and business cases tailored to customer needs
• Navigate buying committees across technical and business stakeholders
• Own opportunities from qualification through signed contract

TECHNICAL ENGAGEMENT

• Understand our product deeply and speak credibly with data scientists and ML engineers
• Communicate technical value in a clear and compelling way
• Shape tailored messages and outreach sequences for technical personas

BUILDING THE MOTION

• Help define qualification criteria, outbound sequences, demo structure, and objection handling - the playbook you help write is what we hire against next
• Bring field intelligence back to the team on objections, positioning, and what customers actually care about

WHAT YOU BRING

• 3+ years in a full-cycle AE role at a data infrastructure, ML tooling, developer tools, or deep tech company
• Track record closing enterprise and mid-market deals with technical buyers — data scientists, ML engineers, or engineering leadership
• Strong technical literacy - comfortable holding a credible conversation with an ML team
• Proven outbound execution and pipeline generation from scratch
• Builder mentality - you want to figure out how to sell this, not inherit someone else's playbook
• Bonus: hands-on experience with data science or ML workflows, or background selling to ML platform teams

WHY THIS ROLE IS EXCITING

• First AE at a company with real research credibility, a Nature publication, and production customers with the world's largest enterprises
• Direct line to founders - you'll shape commercial strategy, not just execute it
• Genuine category creation and bringing frontier AI to industry
• Clear path into sales leadership as the team scales

LOCATION

• This role is specifically located in our Berlin office

LIFE AT PRIOR LABS

You'll work alongside researchers and builders who hold themselves to a very high bar - in the quality of their work and in how they work with each other. We move fast and still take the time to do things right.

Our teams are based in Berlin, Freiburg, and New York - when you're working on something as hard as TabPFN, being in the same room matters. But great people come from everywhere, and in exceptional cases we're open to remote, which usually means frequent travel to one of our offices. Wherever you're based, the whole company comes together regularly for offsites to build and celebrate together.

OUR COMMITMENTS

The best products and teams are built by people with a wide range of perspectives and backgrounds. We welcome applications from all identities and walks of life - especially if you've ever felt discouraged by "not checking every box" - and provide equal opportunities regardless of gender, sexual orientation, origin, disability, or any other trait that makes you who you are.

We care about how your data is handled - see our Recruiting Data Privacy https://priorlabs.ai/recruiting-data-privacy page

We show an excerpt and a link, and the full text when the source allows it. Brands and logos shown belong to their owners.

Source: Statistisches Bundesamt (Destatis), table; dl-de/by-2-0; StarIn's calculation