Head of Embodied AI, Data Foundry

Stord is The Consumer Experience Company, powering seamless checkout through delivery for today's leading brands. Stord is rapidly growing and is on track to double our revenue in the next 18 months. To meet and exceed this target, Stord is strategically scaling teams across the entire company, and seeking energetic experts to help us achieve our mission.

By combining comprehensive commerce-enablement technology with high-volume fulfillment services, Stord provides brands a platform to compete with retail giants. Stord manages over $10 billion of commerce annually through its fulfillment, warehousing, transportation, and operator-built software suite including OMS, Pre- and Post-Purchase, and WMS platforms. Stord is leveling the playing field for all brands to deliver the best consumer experience at scale.

With Stord, brands can increase cart conversion, improve unit economics, and drive sustained customer loyalty. Stord’s end-to-end commerce solutions combine best-in-class omnichannel fulfillment and shipping with leading technology to ensure fast shipping, reliable delivery promises, easy access to more channels, and improved margins on every order.

Hundreds of leading DTC and B2B companies like AG1, True Classic, Native, Seed Health, quip, goodr, Sundays for Dogs, and more trust Stord to deliver industry-leading consumer experiences on every order. Stord is headquartered in Atlanta with facilities across the United States, Canada, and Europe. Stord is backed by top-tier investors including Kleiner Perkins, Franklin Templeton, Founders Fund, Strike Capital, Baillie Gifford, and Salesforce Ventures.

Why This Role:

This is a rare opportunity to build a new AI business from zero, backed by the physical infrastructure, operational footprint, and resources to give it a meaningful head start.

You will have:

• A structural data advantage. Direct access to one of the largest independent e-commerce fulfillment networks in the U.S.—an operational environment that would be extremely difficult for a startup to replicate.

• Direct access to a rapidly growing buyer market. Work directly with the robotics companies, AI labs, and physical AI teams building the next generation of intelligent machines.

• A direct line to the founders. Partner closely with the CTO and Co-Founder, who will join initial customer conversations, help shape the business, and remove internal obstacles.

• True ownership. You won’t be inheriting an established business. You’ll define the strategy, win the first customers, build the product and operation, hire the team, and ultimately own the P&L.

This is an opportunity to build the company that turns real-world operations into the data infrastructure for embodied AI.

What You'll Do:

You will be the first dedicated hire for this initiative, reporting directly to the CTO and Co-Founder. You will have end-to-end ownership of the early Embodied AI business—including the product, customers, data capture program, data operations, team, and P&L.

This is a true builder-operator role. You will:

• Win the first customers. Lead customer discovery and close initial pilots with humanoid robotics companies, AI labs, and physical AI companies. You’ll engage directly with VPs of AI, Heads of Data, and technical leaders to understand their training requirements and translate those needs into products Stord can deliver.

• Define and build the product. Own the data product roadmap across quality tiers—from RGB egocentric video to depth-enhanced and full multimodal datasets with hand pose, annotations, and other metadata. You’ll prioritize what gets built based on customer demand and willingness to pay, while setting and maintaining a high bar for data quality.

• Build and run the capture operation. Stand up the warehouse-based data capture program, including camera and rig selection, participant enrollment, edge processing, data pipelines, and dataset packaging. You’ll coordinate across warehouse operations, engineering, and customers to deliver datasets on spec and on schedule.

• Build the team. Hire and lead a small, highly senior team, scaling it as the business grows. You’ll set the technical direction and quality bar, make architecture and tradeoff decisions, and ensure the team builds the right systems. You don’t need to write every line of code—you need to know what good looks like and make sure it gets built.

Basic Qualifications:

• End-to-end data operations experience. You’ve built or operated a data operation or data product within robotics, AI, or a related technical environment. You understand the full lifecycle—from defining what data to collect and deploying capture hardware, through annotation and quality control, to delivering production-ready datasets to ML teams.

• Technical depth with strong execution. You’re technical enough to lead engineers and make sound architecture decisions, but your greatest strength is getting complex initiatives across the finish line. You can evaluate a pipeline design, understand computer vision and ML data formats, and communicate credibly with both engineers and customers. You don’t need to be the person writing the pipeline—you need to ensure the right pipeline gets built.

• Commercial instinct. You’ve either sold data products to robotics or AI companies, or you’ve been the ML customer buying data and understand what makes a dataset genuinely valuable. You can lead discovery, translate customer requirements into a product specification, and carry an opportunity from first conversation through pilot and close.

• Exceptional operating range. You’re comfortable moving between strategy and execution—negotiating pilot terms in the morning, reviewing an annotation quality report at noon, and briefing a warehouse GM on a deployment that afternoon. You thrive at the intersection of product, commercial, operations, and engineering.

• Deep understanding of physical AI. You understand the physical AI landscape, the needs of humanoid robotics companies and AI labs, and the role that high-quality real-world data plays in training vision-language-action models and deploying robots in production. You understand why egocentric demonstration data is one of the critical bottlenecks to scaling embodied intelligence.