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!