Procedural Data Generation Engineer
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At a glance
Job overview
Vinci4D.ai is hiring a Procedural Data Generation Engineer. Vinci4D.ai is seeking a Procedural Data Generation Engineer to join their data generation team. This role involves building systems that produce synthetic geometries, materials, boundary conditions, and simulation configurations to train and evaluate physics models. The position is deeply technical, focusing on computational geometry, physics simulation, and machine learning to generate diverse, valid, and physically plausible hardware-like geometries and simulation setups.
Key focus areas include Design and build procedural generators for parametric, hardware-like geometry using programmatic CAD or other tools, Generate physically plausible simulation configurations that respect real engineering constraints, and Define and measure diversity and coverage of generated distributions.
Successful candidates bring Experience With Programmatic Geometry. Important skills include Software Engineering, Python, Scripted CAD, B-rep Representations, Mesh Representations, and SDFs. Preferred (not required): CadQuery, OpenCascade, Build123d, and ML Training Pipelines.
Skills & qualifications
Skills
Qualifications
Full job description
About Us
Vinci is building physics AI for hardware design. Our models deliver ultrafast, accurate thermal and mechanical simulation and our platform puts that capability in the hands of every hardware engineer, not just simulation specialists. The quality of our models is bounded by the quality and diversity of the data they're trained on, and that data doesn't exist in the wild. We generate it.
General Description
You will join the data generation team and build the systems that produce the synthetic geometries, materials, boundary conditions, and simulation configurations used to train and evaluate our physics models.
This is a deep technical role at the intersection of computational geometry, physics simulation, and machine learning. The core challenge: write programs that generate families of hardware-like geometries and simulation setups — diverse enough to cover the space our models will see in production, constrained enough that every sample is valid, physically plausible, and useful as training signal.
What you'll do
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Design and build procedural generators for parametric, hardware-like geometry using programmatic CAD (e.g., CadQuery, OpenCascade, Build123d) or another tool of your choice.
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Generate physically plausible simulation configurations — boundary conditions, material assignments, loading scenarios — that respect real engineering constraints.
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Define and measure diversity and coverage of generated distributions, and close the loop between dataset composition and model performance.
Qualifications
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Software engineering skills, especially in Python
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Hands-on experience with programmatic/parametric geometry: scripted CAD, B-rep and mesh representations, SDFs, or procedural generation in graphics tools.
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Solid geometry processing fundamentals: meshing, boolean operations, voxelization/rasterization, and their numerical pitfalls.
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Comfort reasoning about physical validity — you don't need to be a simulation expert, but you should care whether a boundary condition makes sense.
Nice to have
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Exposure to ML training pipelines and how dataset composition drives model behavior
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Familiarity with LLM-driven code generation.
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FEA or thermal engineering experience.
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Familiarity with quality-diversity algorithms, open endedness, deep learning fundamentals.
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Familiarity with the hardware or semiconductor design process.
We're hiring across levels from strong mid-level through staff.
Why Join
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Your work is crucial to the company’s success. Model quality traces straight back to the data generating systems you’ll build.
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Work alongside researchers and engineers spanning ML, numerical methods, and hardware engineering on the frontier of AI.
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Fast-growing startup where you own problems end to end.
You've read the whole posting — now see how you match it.