Machine (Meta) 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 kausables 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 builders 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 its for you Send us your favorite way to drink coffee along with your CV or LinkedIn and well get back to you soon.

If its a match well 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!