Machine Learner

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. Recommended qualifications: * A PhD in ML, Physics, or equivalent — or an MSc with exceptional experience * A strong grasp of causality, meta-learning, PFNs, and active inference * The ability to work independently and think from first principles * Hands-on experience with modern ML tooling (Python, PyTorch) and research workflows * An outcome-oriented mindset Nice to have: * Causal modeling, active learning or Bayesian optimization. * Reinforcement learning, control, time-series modeling or dynamical systems. * Synthetic-data generation, graph-based models or simulation environments. * Publications at NeurIPS, ICML, ICLR or comparable venues. * Meaningful open-source contributions. Benefits -------- 🚀 **Where This Can Go** You will help define kausable's research agenda, not just execute it. As the team grows, there is room to lead a research direction, mentor incoming scientists, and shape how our published work and open-source contributions reach the wider community. And as kausable begins working with its first customers, the research you do here is increasingly likely to leave the lab and reach real-world deployment. 🫂 **Our Culture** We are "Putting Science at the Core of AI" — with all its curiosity, daringness, and humanity. That means we: * are scientists at heart, with a builder's mindset, * are open to challenge, grounded in curiosity and respect, * welcome diverse perspectives and value thoughtful, open debate, * focus on outcomes and real-world impact, * foster an environment of support, inspiration, and freedom for everyone to do their best work. 🏆 **Perks & Benefits** * VSOP equity: a real stake in what we build. * 30 days of paid holiday per year. * Statutory social insurance. * Conference travel and role-relevant learning. * Flexible hybrid work, with roughly one in-person team meet-up per month. * A high-end laptop and access to the compute required to do serious research. ⚒️ **Tools and Infrastructure** * Python, PyTorch, and PyTorch Lightning * Weights & Biases and reproducible experiment workflows. * Docker, AWS, RunPod and comparable cloud infrastructure. 🫶 **Sounds like it's for you?** Send us your favorite way to drink coffee along with your CV or LinkedIn, and we'll get back to you soon. If it's a match, we'll get to know each other over a number of online interviews, followed by an onsite day where we go in depth. We are looking forward to hearing from you! Find [Jobs in Germany](https://www.arbeitnow.com) on Arbeitnow