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
Our foundation model learns physics from simulations, and we generate the simulation data ourselves. The simulations encapsulate real-world, validated physical results. Our aim is to create a data corpus covering multiple types of physics over an exhaustive set of physical objects. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 5+ years building production data or ML pipelines, including at least 2 years where you owned a pipeline that ran on GPUs at meaningful scale
- Experience running large batch or simulation workloads: thousands of long-running jobs, partial failures, retries, preemption, and making sure work is neither lost nor duplicated
- Strong Python and software engineering fundamentals: clean abstractions, typed code, and a test suite you trust
- Comfort with relationalrelationa and NoSQLl databases at scale: schema design, migrations on live data, bulk writes, and query performance once tables reach hundreds of millions of rows
- Experience with workflow orchestration or distributed execution frameworks, such as Temporal, Ray, Airflow, Argo, Kubernetes Jobs or Slurm
- Working knowledge of cloud object storage and of the file formats that feed training at high throughput
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.
Our foundation model learns physics from simulations, and we generate the simulation data ourselves. The simulations encapsulate real-world, validated physical results. Our aim is to create a data corpus covering multiple types of physics over an...
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