---
title: 'Data Partnerships at Generalist'
canonical: 'https://feeny.ai/job/data-partnerships-generalist-san-mateo-tm0yccvmgxm3'
type: 'job'
last_seen: '2026-09-09'
---

# Data Partnerships at Generalist

- **Company:** Generalist
- **Location:** San Mateo, CA / Somerville, MA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-08-31
- **Last confirmed live:** 2026-09-09
- **Apply:** https://jobs.ashbyhq.com/generalist/d5aa97c1-fc3e-439a-9aba-b8c9aaa2dab4

## Job description

## 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 DeepMind, 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, https://research.google/blog/palm-e-an-embodied-multimodal-language-model/ RT-2 https://deepmind.google/blog/rt-2-new-model-translates-vision-and-language-into-action/, Gemini Robotics https://deepmind.google/models/gemini-robotics/), launched and scaled ChatGPT https://chatgpt.com/ and GPT-4 https://openai.com/index/gpt-4-research/ to hundreds of millions of users, engineered the foundations of autonomous driving, built next-generation robots (Atlas https://bostondynamics.com/atlas/, Spot https://bostondynamics.com/products/spot/, Stretch https://bostondynamics.com/products/stretch/) and pushed the limits of what they can do (from parkour https://www.youtube.com/watch?v=tF4DML7FIWk to manipulation https://bostondynamics.com/blog/large-behavior-models-atlas-find-new-footing/, and testing robustness https://www.youtube.com/watch?v=aFuA50H9uek).

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.

## ABOUT THE ROLE

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).

## YOU’LL BE RESPONSIBLE FOR:

- 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 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.
- Travel. Periodic domestic and international travel to partner sites and collection operations.

YOU MIGHT THRIVE IN THIS ROLE IF YOU:

- 6+ years owning external partners or vendors who delivered a product or service to you — supply chain, vendor management, outsourcing/BPO management, data operations, or partnerships with a delivery obligation.
- Demonstrated experience standing up a new vendor from zero: selection, diligence, contracting, ramp, and ongoing management.
- A track record of translating technical or specialist requirements into unambiguous instructions that a non-technical execution partner delivered against successfully.
- Fluency with commercial mechanics — pricing structures, SOWs, acceptance criteria, unit economics — and the judgment to know which terms actually protect quality.
- Exceptional written communication. You will write documents that people you have never met execute without you present.
- Comfort operating with incomplete requirements and re-scoping mid-flight without losing the relationship.
- Even-keeled under pressure and relentlessly consistent on follow-through.

## PREFERRED QUALIFICATIONS

- 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.

## About Generalist

## Company Overview
- **One-liner**: Generalist is a frontier AI research and product company building general-purpose intelligence for the physical world through embodied foundation models for robots.
- **Entity Type**: Private (Series A)
- **Headquarters**: San Mateo, California, United States
- **Founded**: 2024
- **Founders**: Pete Florence (Co-Founder & CEO), Andy Zeng (Co-Founder & Chief Scientist)

## Core Business
- **Primary industry**: Robotics Engineering, Artificial Intelligence, Embodied AI
- **Target customers**: B2B – enterprises operating factories, warehouses, laboratories, restaurants, and logistics centers; eventually homes.
- **Mission**: “Make general-purpose robots a reality. We build embodied foundation models for the physical world.”

## Products & Services
- **GEN-1**: General-purpose AI model for robotics that achieves ~99% average success rate on tasks (vs. 64% for prior state-of-the-art) and completes tasks roughly 3x faster. Trained on half a million hours of real-world data collected via low-cost wearable devices. Available to early-access partners as a model or API. [therobotreport.com](https://www.therobotreport.com/generalist-introduces-gen-1-general-purpose-model-for-physical-ai/)
- **GEN-0**: Previous generation model that demonstrated scaling laws in robotics. Used as the foundation for GEN-1.
- **Data Collection Devices**: Proprietary low-cost wearable hardware that captures human activities at scale, enabling pretraining without large teleoperation or simulation datasets.

## Market Standing
- **Valuation**: Not publicly available
- **Key Metric**: Total Funding – $128M (Series A closed March 2025; earlier seed round led by NVentures) [linkedin.com](https://www.linkedin.com/company/generalistai)
- **Notable Investors/Partners**: NVentures (seed lead); Board Director Ellen Chisa. Series A investor not named in public data.
- **Growth Signals**: Headcount grew 325% YoY to 42 employees. GEN-1 model released five months after GEN-0, with production-level reliability. Early-access partnerships underway. [linkedin.com](https://www.linkedin.com/company/generalistai)

## Competitive Advantages
- **Model performance**: GEN-1 achieves 99% average success rate across tasks like kitting auto parts, folding T-shirts, packing phones – a step change from sub-70% success by previous models.
- **Data efficiency**: Trained primarily on human demonstration data (wearable devices) rather than expensive robot teleoperation data; can match previous models with 10x less task-specific data.
- **Algorithmic innovations**: Custom training kernels, paged attention for real-time inference, Harmonic Reasoning, and RL post-training enable improvisation and recovery from unexpected scenarios.
- **Team pedigree**: Founders and key hires from OpenAI, Google DeepMind, and Boston Dynamics – bringing experience from ChatGPT, GPT-4, PaLM-E, RT-2, Gemini Robotics, Atlas, Spot, and Stretch.

## Strategic Focus
- Current priorities: Scale GEN-1’s embodied foundation model to master more complex physical tasks, expand into economically valuable settings (manufacturing, logistics, healthcare), and build the data infrastructure needed for general-purpose robots. The company is “accelerating the next phase of factories, homes, and the broader physical world.”

## Why Work Here
- **Culture**: Tight-knit, research-forward environment with a mix of AI and hardware engineering. On-site presence in San Francisco (SFO) and Boston (BOS). [generalistai.com/careers](https://jobs.ashbyhq.com/generalist)
- **Remote/Hybrid Policy**: All open positions are listed as “On-site” (no remote or hybrid options).
- **Notable Perks**: Not explicitly disclosed, but the startup’s rapid growth and $128M funding suggest competitive compensation and equity. The team consists of leading roboticists and AI researchers, offering exposure to cutting-edge work.
- **Engineering Culture**: Heavy emphasis on technical staff (Software Engineers in ML Infra, Robotics Controls, ML Optimization, Infrastructure; Research Scientists in pretraining and post-training). Roles include hands-on robot operation and systems building.

## Sources
1. [generalistai.com](https://generalistai.com/)
2. [generalistai.com/careers](https://jobs.ashbyhq.com/generalist)
3. [generalistai.com/about](https://generalistai.com/about)
4. [linkedin.com](https://www.linkedin.com/company/generalistai)
5. [therobotreport.com](https://www.therobotreport.com/generalist-introduces-gen-1-general-purpose-model-for-physical-ai/)

## Other roles at Generalist

- [Research Scientist: Post-Training](https://feeny.ai/job/research-scientist-post-training-generalist-san-mateo-vrnttp54gr4z) — San Mateo, CA / Somerville, MA
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- [Executive Assistant](https://feeny.ai/job/executive-assistant-generalist-san-mateo-d58q9t9a6jav) — San Mateo, CA
- [Data Collection Lab Manager](https://feeny.ai/job/data-collection-lab-manager-generalist-boston-dr2gxr4h4snk) — Boston, MA
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