Machine (Meta) Learner

<p>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.</p> <h2>Tasks</h2> <p>Our research revolves around synthetic world data, deep-learning models trained and validated against it, and capable embedders across domains and modalities. You will:</p> <ul> <li>Shape and pursue research questions around PFNs, meta-learning, in-context learning, representation learning, causality, active learning and adaptive decision-making.</li> <li>Design priors and synthetic task distributions that expose models to useful structure, uncertainty and failure modes.</li> <li>Develop model architectures and training methods for temporal, goal-conditioned and dynamical settings.</li> <li>Build rigorous evaluations, including strong baselines, ablations, calibration tests and out-of-distribution diagnostics.</li> <li>Implement research ideas reliably in Python and PyTorch, and improve the data and experiment pipelines around them.</li> <li>Contribute to top-tier publications, open-source releases and the wider research agenda at kausable.</li> </ul> <h2>Requirements</h2> <p>We are looking for research scientists with a strong background in one or more of:</p> <ul> <li>Deep expertise in PFNs, meta-learning, Bayesian inference, Neural Processes, representation learning, causality, active learning or a closely related area.</li> <li>A record of generating original research hypotheses and testing them with scientific rigor.</li> <li>Strong experimental judgment: you can distinguish optimization failure, prior misspecification and distribution shift.</li> <li>Reliable implementation skills in Python and PyTorch or JAX.</li> <li>A PhD in machine learning, physics, statistics or a related field, or equivalent research experience.</li> <li>The ability to work independently, explain difficult ideas clearly and change your mind when the evidence demands it.</li> <li>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.</li> </ul> <p>Recommended qualifications:</p> <ul> <li>A PhD in ML, Physics, or equivalent — or an MSc with exceptional experience</li> <li>A strong grasp of causality, meta-learning, PFNs, and active inference</li> <li>The ability to work independently and think from first principles</li> <li>Hands-on experience with modern ML tooling (Python, PyTorch) and research workflows</li> <li>An outcome-oriented mindset</li> </ul> <p>Nice to have:</p> <ul> <li>Causal modeling, active learning or Bayesian optimization.</li> <li>Reinforcement learning, control, time-series modeling or dynamical systems.</li> <li>Synthetic-data generation, graph-based models or simulation environments.</li> <li>Publications at NeurIPS, ICML, ICLR or comparable venues.</li> <li>Meaningful open-source contributions.</li> </ul> <h2>Benefits</h2> <p>🚀 <strong>Where This Can Go</strong></p> <p>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.</p> <p>🫂 <strong>Our Culture</strong></p> <p>We are "Putting Science at the Core of AI" — with all its curiosity, daringness, and humanity. That means we:</p> <ul> <li>are scientists at heart, with a builder's mindset,</li> <li>are open to challenge, grounded in curiosity and respect,</li> <li>welcome diverse perspectives and value thoughtful, open debate,</li> <li>focus on outcomes and real-world impact,</li> <li>foster an environment of support, inspiration, and freedom for everyone to do their best work.</li> </ul> <p>🏆 <strong>Perks &#x26; Benefits</strong></p> <ul> <li>VSOP equity: a real stake in what we build.</li> <li>30 days of paid holiday per year.</li> <li>Statutory social insurance.</li> <li>Conference travel and role-relevant learning.</li> <li>Flexible hybrid work, with roughly one in-person team meet-up per month.</li> <li>A high-end laptop and access to the compute required to do serious research.</li> </ul> <p>⚒️ <strong>Tools and Infrastructure</strong></p> <ul> <li>Python, PyTorch, and PyTorch Lightning</li> <li>Weights &#x26; Biases and reproducible experiment workflows.</li> <li>Docker, AWS, RunPod and comparable cloud infrastructure.</li> </ul> <p>🫶 <strong>Sounds like it's for you?</strong> Send us your favorite way to drink coffee along with your CV or LinkedIn, and we'll get back to you soon.</p> <p>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.</p> <p>We are looking forward to hearing from you!</p> <p>Find more <a href="https://www.arbeitnow.com/english-speaking-jobs">English Speaking Jobs in Germany</a> on Arbeitnow</a>