About The RoleTabular 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 — but the hardest problems are still ahead.At Prior Labs, Research Engineers aren't supporting scientists — they are the science team.
You'll design experiments, contribute to papers, and write the code that turns architectural ideas into trained models. We create cutting edge research because the same people do both. As an early team member, 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 scaleBuilding multimodal models that combine tabular, text, and numerical understandingMaking models efficient enough for real-world deployment — not just accurate enough for a paperDesigning architectures for time series, forecasting, anomaly detection, and multiple related tablesDay-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 industryPublications 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 practicesExperience implementing and training neural network architectures — ideally transformers or foundation modelsSolid understanding of training dynamics, scaling behavior, and common failure modes in deep learning systemsGenuine interest in model efficiency — making large models faster, more scalable, and practical to deployNice to Have:Experience at an early-stage startup or as a founding engineerContributions to open-source ML libraries or toolsExperience with model distillation, inference optimization, or on-device MLBackground in tabular data, time series, or other structured data — helpful but not requiredLife at Prior Labs We're a small, ambitious team solving one of the hardest problems in AI, and we're just getting started.
You'll work closely with world-class researchers and builders who care deeply about the quality of their craft, the impact of their work, and the people they work with. We move fast, we think rigorously, and we take the time to do things right. If you're excited by hard problems, motivated by real-world impact, and want to be part of building something that matters, we'd love to hear from you. We're building our teams in Berlin, Freiburg, and New York and we believe that when you're working on something as hard and exciting as TabPFN, being in the same room matters.
Most of our roles are based in one of our offices but great people come from everywhere, and in exceptional cases we're open to remote. This usually involves frequent travel to one of our offices and the whole company comes together regularly for offsites to think, build, and celebrate together.Our CommitmentsWe 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, disability, or any other trait that makes you who you are.We care about how your data is handled. Read our Recruiting Privacy Notice to see exactly what we collect, why, and how long we keep it.