Head of Physical Intelligence Research / Founding Scientist

We're an ambitious early-stage deep-tech startup building the foundations for a world where physical systems can operate, adapt and learn autonomously.

Our mission is to develop uncertainty-aware foundation world models that enable factories, industrial infrastructure and eventually entire physical systems to understand their environments, make decisions and continuously improve without human oversight.

At the heart of our technology is a proprietary physical-data foundry that generates the real-world data needed to train and continuously improve these models.

We envision a future where factories and physical systems can operate intelligently, 24/7, with minimal human intervention, both on Earth and eventually in space.

We're tackling some of the most fundamental challenges in AI: how machines can understand the physical world, reason under uncertainty and learn continuously from real-world interactions.

About the Role

We're looking for a Head of Physical Intelligence Research / Founding Scientist to join our founding team and lead the scientific development of our core technology.

You'll work directly with the founders to define our research agenda, develop novel foundation world models and translate cutting-edge research into intelligent systems capable of operating autonomously in complex physical environments.

This is a rare opportunity to work on fundamental AI research while building the technological foundations of a company from its earliest stages.

We're not looking for someone to simply implement existing research papers. We want someone who can identify fundamental scientific challenges, develop original approaches and lead the research required to solve them.

What You'll Work On

• Define and lead our research strategy in physical intelligence, foundation world models and autonomous learning systems.
• Develop uncertainty-aware world models capable of representing, predicting and reasoning about complex physical environments.
• Research approaches to probabilistic modelling, uncertainty quantification and decision-making under uncertainty.
• Develop foundation models that can generalise across different physical systems, environments and industrial applications.
• Build learning architectures that enable physical systems to adapt and improve continuously through real-world experience.
• Research self-supervised learning, reinforcement learning and model-based decision-making for autonomous physical systems.
• Work with our physical-data foundry to develop scalable approaches to data collection, training and model evaluation.

What We're Looking For

• PhD or equivalent research experience in machine learning, artificial intelligence, robotics, computer science, applied mathematics or a related discipline.
• Strong research background in world models, foundation models, probabilistic machine learning, reinforcement learning or physical AI.
• Deep understanding of uncertainty quantification, probabilistic reasoning or decision-making under uncertainty.
• Experience developing novel machine learning architectures and conducting independent research.
• Strong programming skills in Python and experience with deep learning frameworks such as PyTorch or JAX.
• Experience working with large-scale datasets, model training and experimental evaluation.
• Ability to translate complex scientific ideas into working systems.
• An entrepreneurial mindset and the ambition to build foundational technology with real-world impact.

Particularly interesting research backgrounds include:

• World models and model-based reinforcement learning.
• Bayesian deep learning and uncertainty quantification.
• Generative models and self-supervised representation learning.
• Foundation models for physical systems.
• Causal modelling and physical reasoning.
• Continual learning and autonomous adaptation.
• Physics-informed machine learning.

Why Join Us?

Foundational research: Work on some of the hardest unsolved problems in AI, including how machines can understand, predict and reason about the physical world.

Scientific ownership: Define our research agenda and shape the fundamental architectures behind our technology.

Beyond digital AI: Build intelligence that interacts with and learns from physical environments rather than operating exclusively in the digital world.

From Earth to space: Our long-term ambition extends beyond industrial automation towards autonomous physical systems capable of operating in environments where continuous human supervision isn't possible.

Founding-team opportunity: Join at the earliest stage, influence the company's scientific and technical direction, and help build the research organisation from the ground up.

Why Join Us

We believe the next frontier of artificial intelligence is not simply generating better answers, but building systems that can understand the physical world, reason about uncertainty and act autonomously.

We're looking for a scientist who shares that ambition and wants to turn fundamental research into technology that could redefine how physical systems operate.

If you want to build the intelligence that enables the physical world to run, adapt and learn autonomously, we'd love to hear from you.