Backed by General Catalyst, Khosla and Sequoia.
About the role
Own external data QA & audit: Build and run the end-to-end QA process for multimodal data ingested from third-party vendors and partners — sampling strategy, review workflows, pass/fail decisions, and audit trails — so data is verified before it reaches training. Define quality standards: Partner with researchers to translate what "good data" means into concrete, measurable rubrics and acceptance criteria; keep them versioned and current as research needs evolve.
More about this role
Physical Intelligence is bringing general-purpose AI into the physical world. We are a group of engineers, scientists, roboticists, and company builders developing learning algorithms to create a model that will control any robot to do any task.
Training robot foundation models takes enormous amounts of multimodal data. The Data Operations team makes sure that data is high quality, well understood, and safe to train on. This role owns quality assurance and audit for data, working closely with the researchers who consume it and the teams who create it.
Own external data QA & audit: Build and run the end-to-end QA process for multimodal data ingested from third-party vendors and partners — sampling strategy, review workflows, pass/fail decisions, and audit trails — so data is verified before it reaches training.
Define quality standards: Partner with researchers to translate what "good data" means into concrete, measurable rubrics and acceptance criteria; keep them versioned and current as research needs evolve.
Build the system: Build the software interfaces, tooling, and infrastructure scale the system in an automated way - correctly sampling the right data for QA, internal QA...
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