At kausable, we're pioneering a reasoning-first approach to AI, developing a foundation that's fundamentally different from Large Language Models. While today's models rely heavily on pattern matching and massive pre-training, we're focussing on logical reasoning and adaptability. We are creating agentic AI that learns in-context from just a few examples, opens up whole new domains and skills without retraining, generalizes across tasks and modalities. If you're an experienced researcher, smart, creative, and inspiring – you'll fit right in! Our Company We just raised a large funding round with investors from Black Forest Labs (a pioneer in visual generation), tech experts, and a strong network of advisors and supporters. Tasks Our research and development currently revolves around generating synthetic world data, training and validating deep-learning models, creating and facilitating capable embedders for various domains and modalities. You'll have the opportunity to design and scale deep neural networks pre-trained on synthetic data for active learning and dynamic task solving, develop new model architectures and datasets for real-world reasoning, extend synthetic-data pipelines and evaluate across tasks and modalities, collaborate on open-source initiatives and contribute to research publications. Requirements We're looking for research scientists with a strong background in one or more of these topics: Active Learning Meta-Learning Causality Modelling of complex and evolving systems Reinforcement Learning Bayesian Learning, Bayesian Optimization Our recommended qualifications are: A PhD in ML, Physics, or equivalent - or 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 and research workflows An outcome-oriented mindset Additional nice-to-haves you might bring: Publications in NeurIPS, ICML, ICLR, ECCV/ICCV, or similar Experience with graph-based models or synthetic data generation Open-source contributions Benefits 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 as well as open debate, focus on outcomes and real-world impact, create and foster an environment of support, inspiration, and freedom for everyone to do their best work. Perks & Benefits Competitive salary equity Social insurances (statutory health, pension, etc.) Conference travel Flexible working hours with work-from-home options Pragmatic and supportive coworkers ⚒️ Tools and Infrastructure Python, PyTorch, PyTorch Lightning Weights and Biases Docker RunPod AWS A high-end laptop of your choice Sounds like it's for you? -> ☕ Send us your favorite way to drink coffee along with your CV or LinkedIn! We'll get back to you soon. If it's a match, we'll get to know each other in one to three casual interviews.