Godela (YC X25) · 3 months ago
Founding Simulation Engineer – Godela
Godela is a pioneering company focused on building a Physics Foundation Model, an AI platform designed to predict and simulate physical behavior. They are looking for a Founding Simulation Engineer to define data methodologies and validation strategies to ensure their models accurately reflect the complexities of the physical world.
Artificial Intelligence (AI)SimulationTest and Measurement
Responsibilities
Define the methodology for representing physical systems in data and models—balancing accuracy, scale, and generalization
Own the strategy for validating and verifying our model outputs against ground-truth simulation and experimental data
Generate and curate simulation data across multiple physics domains (CFD, FEA, multiphysics), ensuring high-fidelity coverage of complex behaviors
Build scalable pipelines and standards for turning simulation and experimental data into training-ready datasets
Drive the research and engineering of novel techniques for data augmentation, curation, and generation
Collaborate with ML researchers to integrate new architectures, ensuring data and models align with physical truth
Work directly with the founders to set strategy: which physical behaviors to target, how to represent them, and how to scale methods across domains
Qualification
Required
Hands-on CAD generation and Simulation Expertise: Deep experience with one or more simulation domains (e.g., CFD, FEM, DEM) and commercial or open-source solvers (e.g., Star-CCM+, Fenics/dolfinx, OpenFOAM)
Robust Programming Skills: Strong proficiency in Python is a must. Experience with C++ or other compiled languages for performance-critical tasks is a big plus
Data Pipelining Experience: Proven track record of building and managing data pipelines for large datasets, preferably in a scientific or engineering context
Problem-Solving Mentality: A demonstrated ability to creatively solve complex, unstructured problems
Preferred
Solver development experience
Familiarity with ML frameworks like PyTorch, JAX, or TensorFlow
Experience with parallel and distributed computing for simulation or data processing
Experience with Graph Neural Networks, Physics-Informed Neural Networks, Neural Operators, and Transformer architectures
System-Level Thinking: Experience with cloud computing platforms (AWS, GCP, or Azure) and high-performance computing (HPC) environments (Slurm, PBS)