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Founding Engineer; Multiphysics & Physics AI at GPU-native engineering simulation startup

Job in Greater London, London, Greater London, W1B, England, UK
Listing for: Jack & Jill
Full Time position
Listed on 2026-06-11
Job specializations:
  • Engineering
    Systems Engineer, Software Engineer, AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 60000 - 80000 GBP Yearly GBP 60000.00 80000.00 YEAR
Job Description & How to Apply Below
Position: Founding Engineer (Multiphysics & Physics AI) at GPU-native engineering simulation startup
Location: Greater London

Job Title

Founding Engineer - Multiphysics Simulation & Physics AI

Salary

Not Disclosed

Company Description

London-based deep tech startup building a GPU-native Physics AI platform for engineering simulation and design optimization.

Job Description

You will lead the development of a unified platform that bridges the gap between traditional computational fluid dynamics (CFD) and modern machine learning. By building high-performance multiphysics solvers from scratch and training advanced neural operators, you will empower engineers to develop complex products faster and more sustainably through differentiable simulation.

Location

London, UK

Why this role is remarkable
  • Rare opportunity to build a unified simulation-ML system from first principles, eliminating the traditional wall between CFD experts and AI researchers.
  • Direct impact on a full-stack platform designed to solve the world’s hardest engineering problems across aerodynamics, thermal management, and multiphysics.
  • Work at the bleeding edge of Scientific ML (SciML), utilizing JAX, CUDA, and neural operators to redefine how industrial designs are represented and tested.
What You Will Do
  • Develop and optimize GPU-native multiphysics solvers using finite volume or finite element methods to maximize throughput on HPC systems.
  • Design, train, and benchmark neural operator architectures (like FNO or DeepONet) against existing numerical techniques for engineering applications.
  • Architect systems at the intersection of computational geometry and parallel architectures to underpin how the platform handles complex engineering designs.
The ideal candidate
  • Holds a PhD or equivalent industry experience in computational physics, applied mathematics, or machine learning for scientific applications.
  • Possesses a proven track record of writing production-quality scientific code and numerical methods from scratch, beyond just scripting in existing frameworks.
  • Demonstrates deep expertise in GPU programming (CUDA, JAX, or XLA) and a strong grasp of governing equations like Navier-Stokes and energy equations.
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