Product Engineer

Training robot foundation models takes enormous amounts of multimodal data, much of it collected by third-party vendors and partners around the world. 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 externally sourced 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 checks, tracking dips in data quality from vendors and exposing those insights back to the vendors.

Manage vendor quality loops: Deliver structured, actionable feedback to vendors; coach them on standing up QA checks on their own side; iterate until vendor-side and internal QA agree; enforce standards through review and acceptance processes.

~5 years of experience across building customer facing software.

Vendor management experience: working directly with third-party data providers, delivering actionable feedback, and measurably improving the quality of their output.

Strong ops acumen and drive — a bias to instrument, measure, and iterate quickly rather than crank hours at a broken process.

Comfort being hands-on in the data: reviewing samples yourself, writing and refining rubrics, and spot-checking your own program's output.