Foundation models have transformed text and images, but structured data - the largest and most consequential data modality in the world - has remained untouched. Tables power every clinical trial, every financial model, every scientific experiment, every business decision. No one has built a foundation model that truly understands them.
Until now. What LLMs did for language, we're doing for tables.
Momentum: We pioneered tabular foundation models and are now the world-leading organization in structured data ML. Our TabPFN v2 model was published in Nature and set a new state-of-the-art for tabular machine learning. Since its release, we've scaled model capabilities more than 20x, reached 3M+ downloads, 6,000+ GitHub stars, and are seeing accelerating adoption across research and industry - from detecting lung disease with Oxford Cancer Analytics to preventing train failures with Hitachi to improving clinical trial decisions with BostonGene.
The hardest work is in front of us. We're scaling tabular foundation models to handle millions of rows, thousands of features, real-time inference, and entirely new data modalities - while building the infrastructure to deploy them in production across some of the most demanding industries on earth. These are open problems no one else is working on at this level.
Our team: We’re a small, highly selective team of 20+ engineers and researchers, selected from over 5,000 applicants, with backgrounds spanning Google, Apple, Amazon, Microsoft, G-Research, Jane Street, Goldman Sachs, and CERN, led by Frank Hutter, Noah Hollmann and Sauraj Gambhir and advised by world-leading AI researchers such as Bernhard Schölkopf and Turing Award winner Yann LeCun. We ship fast, create top-tier research, and hold each other to an extremely high bar.
What’s Next: In 2025, we raised €9m pre-seed led by Balderton Capital, backed by leaders from Hugging Face, DeepMind, and Black Forest Labs. The next modality shift in AI is happening - and we're hiring the team that makes it.
You will have ownership over designing, building, and scaling the core infrastructure that brings Prior Labs' foundation models to the world. This is a unique opportunity to make fundamental architectural decisions, establish engineering best practices from the ground up, and profoundly shape the technical direction for serving state-of-the-art AI.
You'll work directly with world-class AI researchers, translating cutting-edge models into reliable, scalable production systems. This role offers significant autonomy and impact, with clear paths to specialize in areas you're passionate about (like ML infrastructure or core backend systems) or grow into a technical leadership position as our team expands. You won't just be implementing features; you'll be building the backbone of our company.
Architect & Design: Design robust, scalable, and secure backend systems and production-grade APIs for serving and finetuning our foundation models.
Build & Implement: Develop high-quality, maintainable code (Python/FastAPI experience highly valued) for core backend services.
Own Infrastructure: Design, deploy, and manage core infrastructure on cloud platforms, focusing on reliability, monitoring, observability, and cost-efficiency.
Core MLOps Concepts: Understanding of the entire machine learning lifecycle (MLLC) from data ingestion and preparation to model deployment, monitoring, and retraining.
Ensure Compliance & Security: Implement secure, GDPR-compliant systems, including data storage, access control, usage tracking, and quota management.
Champion Best Practices: Drive high standards for testing, CI/CD, documentation, and security within the engineering team.
3+ years of professional experience in a cloud engineering, data platform, or SRE role, with a proven track record of managing production infrastructure.
Proven experience building and maintaining data-intensive systems, with a strong understanding of data modeling, storage, and processing technologies.
Strong, hands-on experience with Infrastructure as Code (IaC) using tools like Terraform.
Significant experience with containerization and orchestration technologies (Docker, Kubernetes).
Proficiency in Python.
Experience building or managing infrastructure specifically for machine learning (MLOps, model serving frameworks, feature stores, data pipelines).
Hands-on experience with modern data warehousing and processing platforms like Databricks, Snowflake, or BigQuery.
Contributions to relevant open-source projects.
Offices in Freiburg, Berlin, San Francisco and NYC with flexibility to work across our locations
Competitive compensation package with meaningful equity (We compete with the world's biggest AI companies for talent)
Work with state-of-the-art ML architecture, substantial compute resources, and a world-class team
Annual company-wide offsites to bring the team together (last trip was to the Alps 🏔️)
30 days of paid vacation + public holidays
Comprehensive benefits including healthcare, transportation, and fitness
Support with relocation where needed
We believe the best products and teams come from a wide range of perspectives, experiences, and backgrounds. That’s why we welcome applications from people of all identities and walks of life, especially anyone who’s ever felt discouraged by "not checking every box."
We’re committed to creating a safe, inclusive environment and providing equal opportunities regardless of gender, sexual orientation, origin, disabilities, or any other traits that make you who you are.
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