--- title: 'Research Engineer, Foundation Model at Prior Labs' canonical: 'https://feeny.ai/job/research-engineer-foundation-model-prior-labs-berlin-xvtgn39b0tbe' type: 'job' last_seen: '2026-09-11' --- # Research Engineer, Foundation Model at Prior Labs - **Company:** Prior Labs - **Location:** Berlin, Germany - **Employment:** full-time - **Work type:** onsite - **Posted:** 2025-09-11 - **Last confirmed live:** 2026-09-11 - **Apply:** https://jobs.ashbyhq.com/prior-labs/a1d005f1-732d-41c1-bd23-4e37b4ecdc7b/application **Skills:** Python, PyTorch, Machine Learning, Deep Learning, Neural Network Architecture, Software Engineering Practices, Open-source ML libraries, Model Distillation, Inference Optimization, On-device ML, Time Series Analysis, Tabular Data Processing > Design and build scalable tabular foundation models, contributing to research through experimentation and code. The role involves optimizing transformer architectures for real-world deployment, analyzing scaling behavior, and writing training infrastructure within a small, high-impact team. ## Job description ## 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/s41586-024-08328-6) 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/2025/12/01/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 Tabular data breaks the assumptions that make scaling work for language and vision. There's no natural sequence, no spatial structure, no shared vocabulary across datasets. The architectures and scaling laws that power LLMs don't transfer. We've made the first breakthrough with TabPFN - the hardest problems are still ahead. At Prior Labs, Research Engineers are the science team. You'll design experiments, contribute to papers, and write the code that turns architectural ideas into trained models - the same people do the research and the engineering, which is why both are good. You'll have significant technical ownership and room to grow as we scale. The problems we're solving: - Scaling transformer architectures from 10K to 1M+ samples - without the structural assumptions that make language models scale - Building multimodal models that combine tabular, text, and numerical understanding - Making models efficient enough for real-world deployment, not just accurate enough for a paper - Designing architectures for time series, forecasting, anomaly detection, and multiple related tables Day-to-day, you'll design and test novel architectures, run ablations, analyze scaling behavior, and write the training and evaluation infrastructure that makes rapid experimentation possible. We hold software quality to the same standard as research quality. ## What we're looking for - Master's or PhD in Computer Science or a related field, plus 3+ years of experience building ML systems in research or industry - Publications at top ML venues (NeurIPS, ICML, ICLR, etc.) or equivalent demonstrated research impact (widely used open-source, deployed systems) - Deep proficiency in Python, PyTorch, and the broader ML and data science ecosystem (scikit-learn, pandas, NumPy), with strong software engineering practices - Experience implementing and training neural network architectures, ideally transformers or foundation models - Solid understanding of training dynamics, scaling behavior, and common failure modes in deep learning systems - Genuine interest in model efficiency - making large models faster, more scalable, and practical to deploy ## Nice to have - Experience at an early-stage startup or as a founding engineer - Contributions to open-source ML libraries or tools - Experience with model distillation, inference optimization, or on-device ML - Background in tabular data, time series, or other structured data - helpful but not required 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 ## About Prior Labs ## Company Overview - **One-liner**: Prior Labs builds state-of-the-art tabular foundation models (starting with TabPFN) that natively understand structured data, enabling zero-shot and fine-tuned predictions for spreadsheets, databases, and enterprise data workflows. - **Entity Type**: Private (Pre-Seed/Seed stage; $9.4M total funding) - **Headquarters**: Freiburg, Germany (with offices in Berlin, New York, and San Francisco) - **Founded**: 2024 - **Founders**: Frank Hutter, Noah Hollmann, Alexander Rudolf Diehl, Sauraj Gambhir ## Core Business - **Primary Industry**: Artificial Intelligence / Foundation Models for Tabular Data - **Target Customers**: B2B – Data science teams, enterprise data analysts, quantitative researchers, and agent developers in finance, healthcare, energy, and business analytics. - **Mission / Purpose**: "Help humanity make better decisions" by creating the world’s most capable tabular AI — a foundation for discovery across science, medicine, and the global economy. ## Products & Services - **TabPFN-3 (API)**: Their frontier model for structured data prediction. Handles up to 1M rows in 0.2 seconds. Features a "Thinking mode" that achieves +420 ELO and beats AutoML in 80% of cases on the TabArena benchmark. - **TabPFN-3 (VPC / Private Cloud)**: Deploy the same frontier model inside a customer's own cloud environment for sensitive data (healthcare, finance, defense). - **Agent SDK / Integration**: Embed tabular intelligence into AI agents, enabling structured data reasoning for agentic workflows. - **Synthetic Data Generation**: Generate high-quality synthetic tabular data for privacy-preserving analytics, model training, or data augmentation. ## Market Standing - **Valuation / Market Cap**: Not publicly disclosed (private company). - **Key Financials**: $9.4M in total funding (Seed round closed Feb 2025; corporate/pre-seed rounds prior) - **Notable Investors / Partners**: Balderton Capital, XTX Ventures, SAP Founder Hans-Werner Hector’s Hector Foundation, Atlantic Labs, Galion.exe. Angels include Thomas Wolf (Hugging Face Co-Founder), Peter Sarlin (Silo AI), Guy Podjarny (Snyk/Tessl), Ed Grefenstette (DeepMind), Robin Rombach (Black Forest Labs), and Christopher Lynch (AtScale). - **Growth Signals**: Headcount grew 320% YoY to ~39 employees (from 7 in 2024). Recent acquisition by SAP (signed definitive agreement in 2025). Active job postings: 22. Operates in 10 countries. ## Competitive Advantages - **First-mover in "Tabular Foundation Models"**: While LLMs and vision models are saturated, Prior Labs is pioneering large-scale pre-trained transformers for spreadsheets and databases. - **Breathtaking Performance**: 93% win rate over classic ML on TabArena; beats AutoML in 80% of cases; 0.2s inference on 1M rows — orders of magnitude faster than traditional AutoML pipelines. - **Scientific Lineage**: Founders and team come from top ML institutions (University of Freiburg, Max Planck Institute, Meta FAIR). Scientific advisory board includes Yann LeCun and Bernhard Schölkopf. - **Acquisition by SAP**: Signed definitive agreement — provides immediate enterprise credibility, distribution, and resources for scaling. ## Strategic Focus - **Scale the model**: Continue improving TabPFN-3 with "Thinking mode" and larger context windows for complex reasoning. - **Enterprise adoption**: Expand into healthcare cancer risk prediction, financial trading, and energy forecasting via VPC deployments. - **Agent ecosystem**: Make TabPFN the default AI for structured data in agentic workflows (LangChain, AutoGPT, etc.). - **Post-acquisition integration**: Embed the technology into SAP’s business suite to power predictive analytics for millions of enterprises. ## Why Work Here - **Cutting-edge AI work**: Work with state-of-the-art foundation model architecture, substantial compute resources, and a world-class team (Google DeepMind, Meta, Hugging Face alumni). - **Meaningful impact**: Solve real problems in science, medicine, and finance — not just another chatbot. - **Generous benefits**: 30 days paid vacation + public holidays (paid out if not taken), competitive salary + meaningful equity, seamless relocation support (including relocation bonus), healthcare, transportation, fitness, team lunches & company offsites. - **Office-first culture, multiple hubs**: In-office policy in Freiburg (academic excellence + nature), Berlin (tech/culture), New York (energy/talent), and San Francisco (innovation epicenter). Strong preference for in-person collaboration with top-tier peers. - **Team values**: High integrity, high-performance empathetic culture, undogmatic ("valuing output over principles"), and mission-driven. - **Growth stage**: High growth (320% YoY) + post-acquisition by SAP means accelerated impact, resources, and career development. ## Sources 1. [priorlabs.ai](https://priorlabs.ai/) 2. [priorlabs.ai/careers](https://priorlabs.ai/careers) 3. [priorlabs.ai/about](https://priorlabs.ai/about) 4. [LinkedIn (Prior Labs)](https://www.linkedin.com/company/prior-labs) 5. 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