Research Engineer, Foundation Model

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 - 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 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 ForMaster'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 HaveExperience 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 LabsYou'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 CommitmentsThe 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 page