Expanding a unified model across Maxwell's equations, elasticity, plasticity, Navier-Stokes, nonlinear constitutive systems, and coupled multiphysics interactions.
Evolving a single operator foundation model that generalizes across physical scales and conditioning regimes, not building separate models per equation.

What You Will Own:

Design and refine transformer variants, exploration of sparse and hierarchical attention, and graph-transformer systems for multi-entity interactions.
Scale training and continuous learning with reproducible distributed systems and feedback loops from production environments.
Architect trillion-scale inference using sparse and hierarchical computation while maintaining robustness under diverse industrial workloads.

Engineering Expectations:

Strong software engineering fundamentals with clean abstractions and scalable code design.
Experience with modern ML stacks, including PyTorch and distributed training ecosystems.
Strong CI, regression testing, and validation discipline when evolving core model infrastructure.

Why Vinci:

Single model already deployed across industries with 45TB+ structured training data and billion-voxel inference in production.
High ownership at Series A with the opportunity to define a foundational abstraction layer early.
Build infrastructure that hardware companies depend on daily, with success measured by adoption, throughput, and reliability.

Vinci

Vinci builds operator intelligence infrastructure that modern hardware programs rely on, with a single foundation model deployed across industries on realistic production workloads. They are a Series A company with 45TB+ of physics data, running billion-voxel inference inside Tier-1 semiconductor environments.

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