Run full-resolution simulations in minutes. Vinci’s foundation model for physics unites AI acceleration with verified solvers for as-built accuracy. Backed by Khosla.
About the role
This role is about AI architecture and systems engineering - not low-level GPU kernel work. You will help define and scale the core operator intelligence layer. Billion-voxel inference runs today. You will help design systems that:
What they're looking for
- Large-scale foundation model architecture
- Transformer variants (sparse, hierarchical, graph-based)
- Distributed training systems
- Production ML system design
- Scaling structured datasets
- Writing clean, maintainable, high-quality code
More about this role
Vinci | Full-Time | Remote / Hybrid
At Vinci, we are building the operator intelligence infrastructure that modern hardware programs rely on daily. We have already proven that a single foundation model works out of the box across industries on realistic production workloads.
Trained on 45TB+ of structured physics data
Running billion-voxel inference in production
Deployed inside Tier-1 semiconductor and hardware environments
Operating across multiple physical scales and operator regimes
This is not a research prototype. This is production infrastructure. Now we are scaling deployment at industrial magnitude:
Increase simulation throughput by two orders of magnitude
Move from billion-voxel to trillion-voxel domains
Expand operator coverage across nonlinear regimes
Support global, multi-entity deployment across Tier-1 ecosystems
Our ambition is not to become a frontier AI lab. Our ambition is to become the default operator intelligence layer that hardware companies run on.
Today, our unified model already operates across a subset of partial differential equations in real industrial environments. The next phase is expanding that unified architecture across operators,...
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