Director of Embodied AI, Data Foundry

Stord•Atlanta, GA

About The Position

Stord operates the largest independent e-commerce fulfillment network in the US — 20+ fulfillment centers, 4,000+ warehouse associates, and nearly 100 million packages shipped annually. We are building a new business line that turns this operational infrastructure into some of the most valuable training data assets in physical AI. We are looking for an early leader to build this business from the ground up.

Requirements

Have run a data operation or data product end to end at a robotics or AI data company.
Owned the full lifecycle: defining what data to collect, managing how it gets captured and annotated, deploying hardware capture systems, ensuring quality, and delivering it to ML teams who depend on it.
Technical enough to lead engineers and make architecture decisions.
Core strength is execution across many moving parts.
Able to read a pipeline design and know if it is sound.
Understand computer vision and ML data formats well enough to talk credibly with both engineers and customers.
Commercial instinct: either have sold data products to robotics or AI labs, or have been the ML customer buying data and know exactly what separates a dataset worth paying for from one that is not.
Able to run a discovery conversation, translate customer needs into a product spec, and carry a deal to close.
Operate across altitudes without friction — negotiating pilot terms, reviewing an annotation quality report, and briefing a warehouse general manager on deployments.
Know the physical AI landscape: understand who the humanoid robotics players are, what vision-language-action models need to train, and why egocentric demonstration data is the bottleneck for deploying robots into the real world.

Responsibilities

Own the early Embodied AI business line: the product, the customers, the capture program, data operations, the team, and the P&L.
Run customer discovery and close initial pilot deals with humanoid robotics, labs, and physical AI companies.
Define and own the data product across quality tiers — from RGB egocentric video through depth-enhanced and full multimodal capture with hand pose and annotations.
Decide what gets built, in what order, based on what buyers will actually pay for.
Hold the line on quality.
Stand up the warehouse capture operation: camera and rig hardware selection, enrollment, edge processing, and the processing pipelines that package datasets for delivery.
Coordinate across warehouse operations, engineering, and customers to ship datasets on spec and on schedule.
Hire and lead a small, incredibly senior team, expanding as the business scales.
Set the technical direction and quality bar for the team.
Make architecture and tradeoff decisions.