Startups · AI

Physics Applications - Software Engineer

Vinci · Palo Alto HQ · Remote

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About Vinci

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

Your north star will be production and delivering value to our customers while establishing and maintaining the technical integrity of our codebase.

What they're looking for

  • 8+ years of experience in high-quality software development, with significant experience designing and building production-grade systems
  • Prior experience with scientific computing or physics simulators (FEM, FEA, Molecular Dynamics, FDTD), or large-scale machine learning systems
  • STEM MSc, PhD preferred but not required
  • Demonstrated ability to lead technical initiatives focused on code health, modularity, and system correctness
  • Expertise in building robust, tested, and maintainable software libraries and APIs
  • Strong proficiency in modern software development practices, including system design, agentic coding, testing frameworks, and continuous integration/delivery (CI/CD)
More about this role

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 physics on realistic production workloads.

Trained on PetaBytes of structured physics data

Running billion-voxel inference in production

Tier-1 semiconductor and hardware customers

Operating across multiple physical scales and operator regimes

Increase simulation throughput by two orders of magnitude

Expand simulation capabilities to maximize utility and domain coverage

Support global, multi-entity deployment across Tier-1 ecosystems

Our ambition is to become the default operator intelligence layer that hardware companies run on.

Our proven unified model architecture allows users to rapidly obtain steady state solutions of various partial differential equations. We are expanding this capability to support new physics, new geometries. Beyond that we are building out transient solutions, modeling interactions, deformation and dynamics. A core challenge as we scale out support is designing simple and clean interfaces that turn portions of the codebase into a clean library, ensuring they are...

Read the full posting on Vinci's site ↗

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