Our ambition is to become the default operator intelligence layer that hardware companies run on. To get there, we need the backend and infrastructure that lets our research move to production reliably, repeatably, and at scale.
Our unified model architecture delivers steady-state and transient solutions to partial differential equations at production scale. None of that reaches a customer without robust infrastructure underneath it: reproducible builds, automated pipelines, well-provisioned cloud, and code that holds up under load. This role owns that backbone. You will make our systems fast, dependable, and easy for physicists, researchers, and engineers to build on.
Join a rare early-stage startup that has successfully moved a foundational product from research to real-world, production environments, already serving Tier-1 semiconductor and hardware customers.
This is a unique opportunity for technical and professional growth. You will help define a foundational abstraction layer early in the company’s trajectory. The team is small, friendly, and accessible, and you will be empowered to own and architect large pieces of the system alongside Physicists, AI researchers, Software Engineers, and Computational Geometry experts, including greenfield opportunities to expand Vinci’s core capabilities.
You will work with spectacular technical leaders like CTO Sarah Osentoski and CEO Hardik Kabaria, whose vision is to greatly accelerate physics simulations with ML while retaining solver-grade accuracy.
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.
Vinci is building the operator intelligence infrastructure that modern hardware programs rely on daily. We are scaling our solution to accelerate design validation from hours to seconds. You will build the infrastructure that lets our unified model architecture—currently running billion-voxel inference—scale and expand into the transient domain, a key frontier in modeling interactions, deformation, and dynamics.
Pay not posted
Posted Apr 20, 2026
Pay not posted
Posted Sep 29, 2026
Thinking Machines LabSan Francisco +1
$300K–$400K / yr
Posted Oct 1, 2026
Pay not posted
Posted May 28, 2026
DescriptSan Francisco, CA or Remote +2
$220K–$292K / yr
Posted Sep 4, 2026
Embedding VCMountain View, CA
$140K–$200K / yr
Posted Aug 25, 2026
Source: the Vinci Ashby job board. We last checked this job 8 h ago. We first saw it 4 days ago. A job on a job board does not always mean that the team interviews people now. We found this job board in a public list. We did not yet compare it with the careers page of the company.
This base pay is below the median for infrastructure, platform and SRE jobs in the Bay Area with no level in the job title.
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