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.

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.

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.

🚀 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!

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