Bearcroft
Founding Engineer
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About the company
Our client is an early-stage, VC-backed deep tech company building a full-stack Physics AI platform for engineering.
They combine computational physics, scientific machine learning, GPU-native simulation and high-performance computing to help engineering teams develop better products faster. The company is currently validating its GPU-native simulation platform with early commercial partners and beta users across industry and academia. This is an opportunity to join at an early stage and help define the technical foundations of a company operating at the intersection of simulation, AI and high-performance computing.
The role
They are looking for a Founding Engineer to join as a core member of the technical team and help build the next generation of Physics AI infrastructure and applications. This is not a narrow research role. You would work across the full technical stack, including computational geometry, mesh generation, GPU-native solver development, dataset generation, scientific ML training, optimisation systems and deployment infrastructure.
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The role would suit someone who is excited by the idea of bringing traditional physics-based simulation and modern machine learning together, rather than treating them as separate disciplines.
What you will do
- Continue the development of multiphysics solvers, writing high-quality code and developing numerical methods.
- Train and evaluate Physics AI models, including neural operator models.
- Run fine-tuning experiments, benchmark against existing techniques and iterate on model architectures.
- Engineer complex codebases to maximise throughput on GPU and HPC systems.
- Work across computational geometry, numerical methods and GPU / parallel architectures.
- Help design the systems that underpin how the platform represents engineering designs.
- Develop validation methodologies, benchmarking pipelines and reproducibility frameworks.
- Benchmark against analytical, experimental and industrial reference data.
- Contribute to commercially relevant engineering optimisation workflows.
What they are looking for
The role would suit someone with experience across some of the following areas:
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- Computational physics
- CFD, CHT, FEA or multiphysics simulation
- Solver development
- Numerical methods
- Computational geometry or mesh generation
- GPU computing, CUDA, JAX or PyTorch
- Scientific machine learning
- Neural operators, surrogate modelling or physics-informed ML
- HPC systems and performance optimisation
- Production-quality scientific software
- Validation against experimental or industrial data
What success looks like
Within the first 6-12 months, you will have:
- Contributed production-grade functionality to the multiphysics simulation stack.
- Helped improve solver performance and scalability on GPU systems.
- Built or trained Physics AI models on proprietary simulation datasets.
- Contributed to commercially relevant engineering optimisation workflows.
- Helped shape the long-term technical architecture of the platform.
Package
- £70,000 salary
- Equity
- Early-stage technical ownership
- Opportunity to help build the foundations of a category-defining Physics AI platform
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