Founding Simulation Engineer - Godela

Overview

Join to apply for the Founding Simulation Engineer – Godela role at Godela (YC X25)

This range is provided by Godela (YC X25). Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$120,000.00/yr - $180,000.00/yr

At Godela, we\'re building the first Physics Foundation Model; a physics-informed AI platform learns from simulation, experiment, and equations to instantly predict and simulate physical behavior. At Godela, our vision is to push the boundaries of scientific discovery and engineering innovation. We’re scaling deep-learning surrogates so every engineer has the power of an R&D lab at their fingertips to turn months of simulation and experimentation into minutes.

What You’ll Do

• 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.

What We\'re Looking For

• 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.
• Nice-to-haves: 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).

Seniority level

• Mid-Senior level

Employment type

• Full-time

Job function

• Engineering and Information Technology
• Industries
• Research Services

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