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
Training a foundation model for physics means holding petabytes of simulation data and keeping GPU clusters saturated with it. We are hiring the engineer who will own that layer: the storage where our training data lives, and the compute the AI team trains and validates models on.
What they're looking for
- 10+ years building large-scale distributed systems, including several years running GPU infrastructure for large model training
- Direct experience serving training data at petabyte scale, where throughput and storage cost were both constraints you had to answer for
- Experience setting scheduling and quota policy on shared GPU capacity, with a view on the tradeoff between fleet utilization and how long people wait in the queue
- A track record of building infrastructure whose users are researchers and engineers, and of being measured on what those users were able to do with it, not on the system itself
- Infrastructure you built that survived a substantial change in scale or in the character of the workload, with a clear account of what held up and what you had to replace
- A history of mentoring engineers and of teaching people outside your specialty enough to work on their own
More about this role
Every physical thing you touch exists because somebody successfully navigated the laws of physics: the chips in your phone, the vehicles on the road, the data centers powering AI. Physics determines what can be built, how well it performs, and where it breaks. Yet the tools engineers use to understand physical behavior are too slow and too specialized to use continuously while designing, so critical decisions get made with only a partial view of how a system will behave.
Our mission is to make physical reasoning as accessible to engineers as language became through modern AI. This is not an attempt to build slightly better engineering software. It is an attempt to change how physical products are designed. Our technology is used today by many of the world's most advanced semiconductor and electronics organizations, including nearly half of the twenty largest companies in the industry. We are backed by Khosla Ventures and Eclipse Ventures.
Training a foundation model for physics means holding petabytes of simulation data and keeping GPU clusters saturated with it. We are hiring the engineer who will own that layer: the storage where our training data lives, and the compute the AI...
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