About Augur
Engineering progress is constrained by the speed of simulation. In aerospace, energy, and other safety-critical industries, design cycles are still governed by solvers built for a different era, accurate, but slow and costly, and fundamentally incompatible with real-time decision-making. Augur AI exists to change that constraint.
Founded by Emmanuel and Matthieu, both former researchers at Inria working on AI powered simulation, Augur is building foundational AI models for physical simulation. By combining advances in large-scale model architectures with modern compute, we enable real-time iteration across fluid dynamics, structural mechanics, and electromagnetics, without sacrificing physical fidelity.
We want to push engineering towards real time feedback loops. We want Aircraft components to be optimized aerodynamically in real time. Wind farm layouts to be explored interactively to maximize energy yield. Thermal stress in nuclear infrastructure to be anticipated before it becomes a limiting factor. These will come as the natural consequence of the faster physics simulation we are building.
We are deeply embedded in the European research ecosystem, backed by engineers and founders from organizations including Mistral AI, Dataiku, and DeepMind. As we scale our technology into real-world industrial deployments, we are looking for engineers who want to work at the boundary between frontier AI and the most demanding physical systems on earth. We want to become the new technology layer of modern engineering. Our ambition is global, our first office is in Paris, France.
Your Mission
You will be working on our data generation stack. You will design and scale simulation pipelines to generate the datasets required to pre-train our physics models. You ensure that the synthetic data we feed our models is physically diverse, numerically sound, and computationally efficient.
- Pipeline Architecture: Design and maintain automated workflows that generate large simulation datasets across fluid dynamics and structural mechanics.
- Data Strategy: Collaborate with our AI scientists to find the optimal balance between simulation fidelity and computational cost to create effective pre-training sets.
- Solver Automation: Interface with traditional solvers like OpenFOAM to generate high-quality simulation data.
- Infrastructure Scaling: Deploy and manage these pipelines on modern compute clusters, ensuring seamless data flow into our training environments.
Your Profile
- Simulation Expert: Master’s or PhD in a field focused on numerical methods (CFD, FEA). You have a deep understanding of the Navier-Stokes equations or solid mechanics.
- Headless Operations: Extensive experience configuring and troubleshooting open-source or commercial solvers in scripted, non-GUI modes.
- Python Power User: You are proficient in Python and comfortable with containerization (Docker, Kubernetes) to orchestrate large-scale simulation runs.
- Data Fluent: You understand data normalization, mesh-to-tensor conversions, and the nuances of handling large-scale scientific datasets.
Why Join Us?
- Culture: Small team of committed top scientists and engineers in their field.
- High Impact: Join a founding team where your work directly shapes the product and its deployment with global leaders. Work on physical AI that delivers immediate, real-world value, making planes, windfarms, and nuclear plants more efficient and sustainable.
- Frontier AI: Work at the intersection of Generative AI, High-Performance Computing, and complex physics.
- Direct Influence: As one of our first Engineers, you will have a massive say in our roadmap and how our user interface evolves to meet engineering needs.
- Momentum: Join us at a key turning point as we scale from technical validation to major commercial expansion.