Machine (Meta) Learner

Job description

At kausable, we build causal, reasoning-first models that learn from a handful of examples and adapt without retraining. We are looking for a research scientist to advance the foundations of that approach, with a particular focus on Prior-Data Fitted Networks, meta-learning and the priors that determine what our models can learn. This is a research role with real implementation responsibility. You will form hypotheses, build the systems needed to test them and turn strong results into reproducible research, open-source work and production-relevant capabilities. Tasks Our research revolves around synthetic world data, deep-learning models trained and validated against it, and capable embedders across domains and modalities. You will: Shape and pursue research questions around PFNs, meta-learning, in-context learning, representation learning, causality, active learning and adaptive decision-making. Design priors and synthetic task distributions that expose models to useful structure, uncertainty and failure modes. Develop model architectures and training methods for temporal, goal-conditioned and dynamical settings. Build rigorous evaluations, including strong baselines, ablations, calibration tests and out-of-distribution diagnostics. Implement research ideas reliably in Python and PyTorch, and improve the data and experiment pipelines around them. Contribute to top-tier publications, open-source releases and the wider research agenda at kausable. Requirements We are looking for research scientists with a strong background in one or more of: Deep expertise in PFNs, meta-learning, Bayesian inference, Neural Processes, representation learning, causality, active learning or a closely related area. A record of generating original research hypotheses and testing them with scientific rigor. Strong experimental judgment: you can distinguish optimization failure, prior misspecification and distribution shift. Reliable implementation skills in Python and PyTorch or JAX. A PhD in machine learning, physics, statistics or a related field, or equivalent research experience. The ability to work independently, explain difficult ideas clearly and change your mind when the evidence demands it. We are primarily hiring at senior level. We are also open to exceptional candidates with fewer years of experience who can demonstrate comparable depth, judgment and ownership. Recomme…