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Design and build procedural generators for parametric hardware-like geometry and physically plausible simulation configurations. Define and measure the diversity of generated distributions to improve physics model performance.
Requires strong software engineering skills in Python and hands-on experience with programmatic geometry and geometry processing fundamentals. Candidates should be comfortable reasoning about physical validity and engineering constraints.
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.
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.
Design and build procedural generators for parametric, hardware-like geometry using programmatic CAD (e.g., CadQuery, OpenCascade, Build123d) or another tool of your choice.
Generate physically plausible simulation configurations — boundary conditions, material assignments, loading scenarios — that respect real engineering constraints.
Define and measure diversity and coverage of generated distributions, and close the loop between dataset composition and model performance.
Software engineering skills, especially in Python
Hands-on experience with programmatic/parametric geometry: scripted CAD, B-rep and mesh representations, SDFs, or procedural generation in graphics tools.
Solid geometry processing fundamentals: meshing, boolean operations, voxelization/rasterization, and their numerical pitfalls.
Comfort reasoning about physical validity — you don't need to be a simulation expert, but you should care whether a boundary condition makes sense.
Exposure to ML training pipelines and how dataset composition drives model behavior
Familiarity with LLM-driven code generation.
FEA or thermal engineering experience.
Familiarity with quality-diversity algorithms, open endedness, deep learning fundamentals.
Familiarity with the hardware or semiconductor design process.
We're hiring across levels from strong mid-level through staff.
Your work is crucial to the company’s success. Model quality traces straight back to the data generating systems you’ll build.
Work alongside researchers and engineers spanning ML, numerical methods, and hardware engineering on the frontier of AI.
Fast-growing startup where you own problems end to end.
A frontier lab building the foundation model for the physical world, already deployed in flagship engineering programs.
Vinci is a frontier lab building the foundation model for the physical world. The company is developing deterministic, solver-grounded systems that make physics continuously computable, shifting engineering from episodic simulation to continuous physics infrastructure. Already deployed inside production engineering workflows and running on flagship programs, Vinci operates directly on native design and manufacturing geometry to enable high-fidelity physics reasoning without the traditional burden of manual setup, meshing, and specialist-only access. Rather than functioning as a faster point tool, Vinci changes the operating model of engineering by making physics more available, more repeatable, and more actionable across the organization. Its systems support design, verification, manufacturing, and reliability decisions, helping teams evaluate more scenarios, surface physical risk earlier, and improve engineering leverage without scaling specialist simulation teams linearly. Vinci’s broader aim is to make physics a shared reasoning layer for designing, building, and operating the physical world.
Offices: 316 High St, Palo Alto, California 94301, US
View all jobs at Vinci4D.aiBased on 548 listings with disclosed salaries, most technology jobs in Palo Alto, CA pay between $120k–$264k per year. Individual offers vary with seniority, company size, and specialization.
There are currently 736 open technology positions in Palo Alto, CA listed on Clera. New openings are added daily as companies post roles.
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