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
Own the Release Lifecycle: Act as the final gatekeeper for production deployments, managing versioning, coordinating "Go/No-Go" decisions, and overseeing deployment pipelines and customer installations. Bridge Engineering & Product: Collaborate with AI/ML experts, thermal/mechanical engineers, and software developers to translate complex technical updates into actionable insights and release notes for both technical and non-technical customers.
What they're looking for
- Quality Expertise: 6+ years of experience in software quality engineering with a mastery of designing comprehensive test plans. You excel at defining the "what" and "how" of a test suite before any code is written
- Test Architecture & Logic: The ability to translate complex engineering requirements into structured, unambiguous test logic. You can design clear procedural instructions that serve as a scalable blueprint for both manual execution and automation
- The "Gatekeeper" Mindset: Exceptional attention to detail and the analytical ability to perform rigorous risk evaluations. You can weigh technical defects against deployment schedules to make informed "Go/No-Go" decisions during fast-paced release cycles
- CI/CD & DevOps Literacy: Direct experience managing deployments within a Linux/Debian environment using tools such as GitHub Actions, Docker, or Jenkins
- Engineering Domain Knowledge: Familiarity with CAD/CAE data formats (GDSII, OASIS, STEP, ECXML, IPC) or basic concepts in thermal/mechanical simulation
- HPC Experience: Background in testing high-performance computing applications or software that heavily utilizes GPU acceleration
More about this role
Vinci is building the intelligence layer for hardware engineering. For decades, physics has been one of the biggest constraints on how physical products get designed — not because engineers don't trust it, but because it's been too slow and too expensive to use continuously. Teams make hundreds of design decisions before they ever see high-fidelity physics; by the time the simulation arrives, the design is largely locked.
We change that. Our Foundation Model for Physics makes deterministic physical reasoning available while a design is still evolving, so engineers can make better decisions before the cost of change compounds. Unlike general-purpose AI, our model learns physical behavior from first principles — not from human-generated text or images.
Today, Vinci is deployed on production engineering programs at leading semiconductor companies — the hardest physics problems in modern electronics. Semiconductors are not the limit. They're the proof: our ambition includes everything downstream of the chip, from robots and vehicles to data centers and aircraft.
We're growing fast, and hiring across the company. Joining now means arriving early enough to help shape how Vinci works,...
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