Founding Simulation Engineer – Godela

Godela (YC X25)

San Francisco (CA)

On-site

USD 120,000 - 180,000

Full time

14 days+

Get more replies from employers

Send a job-specific resume in minutes.

Job summary

An innovative AI-focused tech startup in San Francisco is seeking a Founding Simulation Engineer to define methodologies for data representation and validate model outputs. The ideal candidate has deep expertise in simulation domains and robust programming skills, particularly in Python. This full-time position offers a competitive salary range of $120,000.00 to $180,000.00 per year, based on experience and skills.

Qualifications

Deep experience with simulation domains like CFD or FEM.
Strong proficiency in Python, experience with C++ is a plus.
Proven track record of building data pipelines for large datasets.

Responsibilities

Define methodology for representing physical systems in data.
Own strategy for validating model outputs against ground-truth data.
Build scalable pipelines for training-ready datasets.

Skills

CAD generation and Simulation Expertise

Robust Programming Skills in Python

Data Pipelining Experience

Creative Problem-Solving

Familiarity with ML frameworks

Experience with parallel computing

Tools

Star-CCM+

OpenFOAM

JAX

TensorFlow

Job description

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