Your past work might include:

• Diffusion models, score-based generative methods, or GANs
• Foundation model design (attention mechanisms, tokenisation, scaling laws)
• Long-context or irregular time series modelling (e.g., EEG, spatiotemporal data)
• Reinforcement learning or decision-making frameworks

Profile 2 — AI for Science Researchers

You’re a scientist who has integrated modern AI into a scientific domain—be it physics, chemistry, climate, neuroscience, or another field. You’ve either built domain-specific deep learning models or adapted frontier architectures to simulate or explain natural phenomena. You’re fluent in both theory and application, and you’ve published in scientific or AI journals.

Your background might include:

• Molecular modelling, simulation surrogates, physics-informed ML, or brain modelling
• Training GNNs, transformers, or diffusion models on scientific data
• Scientific computation with a data-centric, generative, or learning-based twist
• Collaborations in academia, specialised startups, or big tech research teamsWe’re currently not actively hiring, but want to engage with researchers who see themselves thriving in this kind of environment. Our next wave of hires may begin in early 2026, with some flexibility for exceptional fits.