Data Partnerships

About Generalist

At Generalist, we are on a mission to build general intelligence for the physical world and make it useful to everyone. We believe the industries and homes of the future will depend on humans and machines working together in new ways. Robots can help us build more and get more done.

We build embodied foundation models, starting with a focus on dexterity. This requires advancing the frontiers of data, models, and hardware, to enable robots to intelligently interact with the physical world.

The company embraces both large-scale AI and robotics as core to its DNA. Our team of researchers, roboticists, and company builders come from OpenAI, Boston Dynamics, Google Deep Mind, and other frontier labs—with a track record of shipping AI breakthroughs. Before Generalist, we pioneered large embodied multimodal models and vision-language-action models (PaLM-E, RT-2, Gemini Robotics), launched and scaled ChatGPT and GPT-4 to hundreds of millions of users, engineered the foundations of autonomous driving, built next-generation robots (Atlas, Spot, Stretch) and pushed the limits of what they can do (from parkour to manipulation, and testing robustness).

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

Embodied foundation models and their capabilities are driven by data — and robotics data does not exist on the internet waiting to be scraped. It has to be manufactured by people doing real work in real environments, to a spec guided by our model science and scaling laws.

You will own the data engine that feeds our models: finding the right partners who can collect at quality and volume, standing them up, supplying them with necessary hardware, and holding them to a bar. Our data partnerships and operations extend globally, and expanding the ecosystem involves building the underlying network infrastructure for data ingestion and distribution that will feed in to support future commercial partnerships as well.

Expect to extend the data engine into new geographies and new data modalities on tight timelines.

The hardest part of the job is translating model needs and data requirements into something a data partner can execute without you in the room (and you are the first to catch the drift when what comes back is technically compliant but practically useless).

• Data supply strategy. Identify what we need to buy versus build. Map the vendor landscape across data types and quality. Bring recommendations, not options.
• Vendor sourcing and diligence. Find, evaluate, and pilot new data partners. Run structured trials before committing volume. Know how to tell a real capability from a good deck.
• Requirements elicitation. Sit with ML and engineering leads and extract the actual requirement — volume, environments, embodiments, task diversity, annotation schema, acceptance criteria, what happens downstream. Most of the time the requirement does not exist in writing until you write it.
• Translation and specification. Author partner-facing specs that a non-ML operator can execute against with no follow-up call. Define the unit of delivery, what passes, what fails, and what to do when it is ambiguous.
• Commercial terms. Structure pricing, rate cards, minimums, milestones, and acceptance language. Work with legal on MSAs and SOWs. Own the unit economics and know what we are paying per unit of usable data, not per unit delivered.
• Scaling a partnership into a network. When one partner hits capacity, stand up the next without dropping quality or blowing up cost.
• Operating…