Member of Technical Staff - ML Ops
Vinci, Palo Alto HQ
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.
Our data corpus is expanding. We currently operate on the petabyte scale, and our data volume is growing rapidly. We are hiring the engineer who makes that possible: the person who owns the pipeline from "here is a generation recipe" to "here is a versioned, validated dataset a training run can consume," and the ML lifecycle around it. Your job is to expand our job scheduling and execution platform to ensure that campaigns of millions of simulations, ML training runs, and future jobs are simple to run, fault-tolerant, and well-cataloged for traceability.
This is hands-on engineering in a production codebase. You will write, test and ship the code yourself.
What you will do
- Scale the data generation pipeline by orders of magnitude. Our generation framework runs real simulations on 3D parts, and we are scaling from thousands of simulations to millions.