Computational Fluid Dynamics Engineer

Founding Engineer | Physics AI | CFD | GPU | Scientific ML London Tired of running other people's solvers? Come build your own. Why consider this role?

• Build a GPU-native CFD solver from scratch, not just maintain legacy code
• Train Physics AI models on data from your own simulations
• Founding equity, so you actually own a piece of what you build
• Up to £150k salary depending on experience
• Work directly with the founders and shape the whole architecture
• Real customers in aerospace and automotive already testing the platform
• Backing to publish your work This is a VC-backed deep tech startup in London, building a full Physics AI platform for engineering. Their own solver runs CFD and heat transfer on GPUs. That creates loads of simulation data, which trains AI models that predict results in seconds instead of hours. They're hiring a small group of founding engineers, so you'd be one of the first, not hire number 50. You won't be stuck in one lane either. One week it's solver numerics, the next it's training a neural operator, the next it's squeezing more speed out of the GPUs. No more wall between the simulation people and the ML people. You get to do both. You'll ideally have:
• A PhD in computational physics, applied maths, mech/aero engineering or ML for science (or the industry equivalent)
• Written real simulation code yourself (finite volume, finite element, multigrid etc)
• Strong Python plus CUDA, JAX or C++
• Navier-Stokes and heat transfer at the equations level
• Some scientific ML (FNO, DeepONet, PINNs, GNNs)
• Strong coding skills Bonus if you've worked on aero, thermal or multiphysics problems, or you've got papers out. If you're as happy debugging a pressure-velocity coupling as you are tuning a neural net, you'll love this. No need for an up-to-date CV just yet, just click 'easy apply' and someone will reach out with more details. We can then help you build the best CV for the role later. 🚀