- Robot type
- Robot AI and Software
- Location
- USA
- Job type
- Commercial Operations
- Posted
- Aug 31, 2026
Full-time
Data Partnerships
Job description
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 DeepMind, and other frontier labs—with a track record of shipping AI breakthroughs.
At Generalist, we are on a mission to build general intelligence for the physical world and make it useful to everyone.
Job responsibilities
- 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.
- 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.
- Scaling a partnership into a network. When one partner hits capacity, stand up the next without dropping quality or blowing up cost.
- Operating cadence. Trackers, weekly partner reviews, forecasts against the research roadmap, and a clear picture of cost, volume, and quality that anyone at the company can read.
- Closing the loop. Report back to research on what the data actually produced and use it to change the next cycle's spec.
Job requirements
- Experience buying or managing data collection, annotation, or labeling at scale — managed workforce, crowd, or BPO.
- Experience close to an ML or research organization, and enough working understanding of training data to push back on a request rather than just relay it.
- Background in autonomous vehicles, robotics, hardware, or another domain where physical-world data acquisition is a first-class problem.
- Experience at a company that grew quickly enough that the process you inherited stopped working and you had to rebuild it.