Data Partnerships

Generalist•Somerville, MA

About The Position

Generalist is building embodied foundation models for the physical world, with an initial focus on dexterity. The Data Partnerships role owns the data engine that supplies these models, including finding and qualifying partners that can collect data at the required quality and volume, onboarding them, supplying necessary hardware, and maintaining execution standards. The work spans global data partnerships and operations, and includes expanding into new geographies and data modalities while developing infrastructure for data ingestion and distribution that can also support future commercial partnerships. The role translates model-science and research needs into partner-executable data collection requirements. It covers sourcing and evaluating vendors, piloting partnerships, writing clear specifications and acceptance criteria, structuring commercial terms, managing unit economics, scaling the partner network, and establishing operating cadences that make cost, volume, and quality visible. The role also closes the feedback loop by communicating data outcomes to research and using them to inform future specifications. Periodic domestic and international travel to partner sites and collection operations is expected.

Requirements

At least 6 years owning external partners or vendors with a delivery obligation, such as in supply chain, vendor management, outsourcing/BPO management, data operations, or partnerships.
Demonstrated experience standing up a new vendor from selection and diligence through contracting, ramp, and ongoing management.
Track record of translating technical or specialist requirements into unambiguous instructions successfully executed by a non-technical partner.
Fluency with commercial mechanics, including pricing structures, SOWs, acceptance criteria, and unit economics, and judgment about terms that protect quality.
Exceptional written communication, including the ability to write documents that people can execute without the writer present.
Comfort operating with incomplete requirements and re-scoping during execution while maintaining partner relationships.
Even-keeled under pressure and consistent follow-through.

Nice To Haves

Experience buying or managing data collection, annotation, or labeling at scale through managed workforces, crowds, or BPO providers.
Experience working close to an ML or research organization, with enough understanding of training data to challenge requests rather than simply relay them.
Background in autonomous vehicles, robotics, hardware, or another field where physical-world data acquisition is a primary concern.
Experience at a rapidly growing company where inherited processes stopped working and needed to be rebuilt.

Responsibilities

Develop data supply strategy, including deciding what to buy versus build and mapping vendors by data type and quality.
Source, evaluate, and pilot data partners; conduct structured trials before committing volume.
Elicit actual data requirements from ML and engineering leads, including volume, environments, embodiments, task diversity, annotation schema, acceptance criteria, and downstream needs.
Write partner-facing specifications that non-ML operators can execute independently, defining delivery units, pass/fail criteria, and how to handle ambiguity.
Structure commercial terms such as pricing, rate cards, minimums, milestones, and acceptance language; work with legal on MSAs and SOWs.
Own unit economics and track the cost per unit of usable data rather than merely per unit delivered.
Scale partnerships into a network by adding partners when capacity is reached while preserving quality and cost control.
Maintain operating trackers, weekly partner reviews, forecasts against the research roadmap, and clear reporting on cost, volume, and quality.
Report data outcomes to research and use feedback to revise specifications for subsequent collection cycles.
Travel periodically, domestically and internationally, to partner sites and collection operations.
Stand up new partners, including selection, diligence, contracting, ramp, and ongoing management.