Simulation Engineer (CFD) - Large Physics Models

About the role

Large Physics Models need data, and the quality, diversity and cost of that data shape is what we need to define. As a Simulation Engineer in our Research team, you will build the simulation side of that foundation. You’ll work in a research environment, alongside ML researchers and engineers, on how physics-based simulation and machine learning can be combined in ways that haven’t been done before.

You’ll be working at the frontier of Large Physics Model development, shaping new AI paradigms for our customers to benefit from in the future. You will aid in data generation, how, and at what fidelity, so that the models we build are both accurate and affordable to train.

What you will do

• Generate simulation data for training large-scale ML models, drawing on a mix of methods and fidelity levels.
• Develop and implement solvers and numerical methods, potentially in modern differentiable or GPU-native frameworks such as JAX or NVIDIA Warp.
• Work with multi-fidelity data: understand tradeoff between fidelity and CFD simulators and how the two can be combined.
• Use commercial and open-source CFD tools to produce reference data
• Collaborate closely with ML researchers and engineers, translating between the physics and the models, and helping the team get the best corpus possible.
• Contribute to the technical direction of our data generation work and flag the simulation investments that will unlock research progress.

What you bring

• A postgraduate degree (MSc or PhD) in engineering, applied mathematics, physics or a related field, ideally with research involving numerical analysis of PDEs or computational engineering. A PhD is preferred but not required.
• At least 1-2 years of industry experience in CFD, ideally in turbomachinery, aerospace, defence or the chemical and process industries (CPI).
• Experience developing numerical solvers or PDE solvers, for example in JAX, Warp or a comparable framework.
• Hands-on experience with at least one commercial CFD solver, such as Simcenter STAR-CCM+ or Ansys Fluent.
• Comfort working in a research setting, where problems are open-ended and the path isn’t always clear.

Strongly preferred

• Practical experience with OpenFOAM.
• Exposure to ML or surrogate model development, or experience working directly with ML teams.

Why join us

• Work at the frontier of Large Physics Models, in a team where simulation expertise shapes the research direction.
• Join a research environment with real access to ML talent and compute.
• Influence how we think about data, fidelity and physics in AI.