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NP Company développe des modèles d’intelligence artificielle appliqués à la simulation physique pour les secteurs de l’aéronautique, de la défense et de l’énergie.

Repères sur NP Company
Secteur
Logiciels et Internet
Type d’entreprise
Financée par du capital-risque
Domaine officiel
npco.ai
Création
2025 · 1 an
Dernière levée
Seed · 2026-06-01
Montant annoncé
7 000 000 $
Offres ouvertes
7

Investisseurs mentionnés

Détails de l’offre

La description complète publiée par NP Company.

Voir l’offre originale ↗

Description de l’offre

About NPco

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. NPco exists to change that constraint.

Founded by Emmanuel and Matthieu, both former researchers at France's national institute for research in digital science and technology (Inria) working on AI powered simulation, NPco 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 Build and scale mechanical simulation pipelines that generate accurate structural datasets for training and evaluation.

Simulation Pipelines: Build structural and mechanical simulation pipelines (FEA, impact, fatigue).

Model Fidelity: Ensure accurate material models, boundary conditions, and load cases.

Workflow Scaling: Scale data generation workflows with robust automation and QA checks.

Data Translation: Translate simulation outputs into model-ready datasets and metrics.

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.

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Prérequis

  • Master’s or PhD in a field focused on numerical methods
  • Deep understanding of the Navier-Stokes equations or solid mechanics
  • Extensive experience configuring and troubleshooting open-source or commercial solvers in scripted, non-GUI modes
  • Proficient in Python
  • Comfortable with containerization
  • Understanding of data normalization
  • Understanding of mesh-to-tensor conversions

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