Research Scientist, Foundation Model

Research Scientists at Prior Labs advance foundation models for structured data. You'll develop models and learning methods that generalize across datasets and tasks, helping identify worthwhile research questions, design experiments, and take ownership of projects through to publications and model releases.

What you'll do

• Develop models and learning methods that generalize across datasets and tasks.
• Identify worthwhile research questions and design experiments that distinguish between competing explanations.
• Take ownership of projects through to publications and model releases.
• Implement and run your own experiments, interpret the results, and work closely with research engineers to turn promising ideas into reliable models.
• Work on problems including dataset size and context length, multimodal models combining tabular data with text, time series and forecasting, relational models across multiple tables, causal inference, and recommender systems.

What you'll need

• A track record of original research contributions demonstrated through publications at leading venues, influential open-source work, benchmarks, or deployed methods.
• Strong experience developing and analyzing machine learning models, with the ability to implement, train, and debug your own methods in PyTorch.
• Solid understanding of training dynamics, generalization, and common failure modes in deep learning systems.
• Strong experimental judgment: you choose appropriate baselines, investigate unexpected results, and distinguish robust improvements from evaluation artifacts.
• Excellent engineering fundamentals and strong Python skills, with a track record of writing high-quality research code.
• Intellectual honesty and constructive collaboration: you explain your reasoning, acknowledge limitations, take feedback seriously, and change your approach when the evidence calls for it.

Nice to have

• Research experience in tabular data, time series, relational learning, causal inference, or recommender systems.
• Experience at an early-stage startup or research lab that regularly releases models or systems.
• Contributions to open-source ML libraries or tools.
• Experience with model scaling, distillation, inference optimization, or efficient architectures.

What they offer

• Work alongside researchers and builders at a high bar for quality and collaboration.
• Based in Berlin, Freiburg, and New York with flexibility for exceptional remote cases.
• Regular company offsites to build and celebrate together.