---
title: 'Research Assistant at Generalist'
canonical: 'https://feeny.ai/job/research-assistant-generalist-san-mateo-1ahfzy22gp7s'
type: 'job'
last_seen: '2026-09-09'
---

# Research Assistant at Generalist

- **Company:** Generalist
- **Location:** San Mateo, CA / Somerville, MA
- **Compensation:** $150k–$200k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-06-25
- **Last confirmed live:** 2026-09-09
- **Apply:** https://jobs.ashbyhq.com/generalist/fc7c7b49-248a-4849-a473-a0bd246e5486/application

**Skills:** Experimental Design, Data Collection, Statistical Analysis, Robotics, Machine Learning, Programming, Data Analysis, Hardware, Electronics, Mechanical Assembly, Experimental Tooling

> The Research Assistant runs real-world experiments at the intersection of robotics and machine learning, focusing on physical-world evaluation. Responsibilities include designing evaluation tasks, collecting data, measuring success rates, and documenting workflows to ensure rigorous and reliable results for robot...

## 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:

We are looking for a Research Assistant to help design, run, and analyze experiments at the intersection of machine learning and robotics. This is an entry-level research role for individuals with less than 3 years of research experience, and is designed to be a potential career path towards eventually contributing as a Research Scientist.

At Generalist, we are building foundation models for robots. These models improve through a tight feedback loop: design experiments, collect data, train or fine-tune models, evaluate them in the real world, analyze results, and repeat. This role helps make that loop faster, more rigorous, and more reliable.

You will work closely with ML researchers and robotics engineers to run robot experiments, design evaluation tasks, brainstorm ideas, collect data, interpret results, and document repeatable workflows.

A major part of this role is helping ensure our evaluations are trustworthy. We care deeply about experimental design, controls, hands-on iteration, sample sizes, variance, repeatability, and statistical rigor.

## YOU’LL BE RESPONSIBLE FOR:

- Running structured experiments on robot platforms
- Setting up physical tasks, materials, fixtures, and benchmarks for robot evaluations
- Collecting high-quality robot data and tracking experimental conditions
- Measuring real-world success rates across tasks, robots, and model variants
- Designing evaluations with attention to controls, repeatability, statistical rigor, and sources of bias
- Analyzing results to help distinguish real model improvements from noise
- Synthesizing findings and communicating them clearly to ML researchers and engineers
- Preparing robots, sensors, workspaces, and materials for rollouts and evaluations
- Helping kick off training jobs, run evaluations, and organize results
- Beta testing internal and third-party tools for teaching robots new skills
- Troubleshooting physical setups, hardware issues, and procedural bottlenecks
- Writing clear documentation and playbooks so others can reproduce workflows
- Improving experimental reliability, data quality, and operational throughput over time

YOU MIGHT THRIVE IN THIS ROLE IF YOU:

- Have experience running experiments, lab studies, field studies, data collection workflows, or structured evaluations
- Think carefully about experimental design, confounding factors, controls, sample sizes, variance, and what conclusions the data can actually support
- Are diligent and detail-oriented, especially when tasks are repetitive but subtle differences matter
- Enjoy hands-on work with physical systems, equipment, materials, or instruments
- Are comfortable following protocols while also noticing when something is wrong or could be improved
- Can coordinate many moving parts: robots, materials, tasks, data, model versions, metrics, and documentation
- Communicate clearly and can summarize what happened, what changed, and what the evidence suggests
- Are curious about machine learning and robotics, even if you are not yet an expert in either
- Have some exposure to programming, data analysis, robotics, hardware, electronics, mechanical assembly, or experimental tooling
- Prefer fast iteration, careful measurement, and empirical progress over abstract theory alone

## 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
- [Data Partnerships](https://feeny.ai/job/data-partnerships-generalist-san-mateo-tm0yccvmgxm3) — San Mateo, CA / Somerville, MA
- [Mechanical Engineer](https://feeny.ai/job/mechanical-engineer-generalist-boston-74p3xtqpvs13) — Boston, MA
- [Electrical Engineer](https://feeny.ai/job/electrical-engineer-generalist-san-mateo-x985d69q4w17) — San Mateo, CA / Somerville, MA
- [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
- [Office Manager](https://feeny.ai/job/office-manager-generalist-san-mateo-nmvjjw2x5jpv) — San Mateo, CA
- [Engineering Technician](https://feeny.ai/job/engineering-technician-generalist-san-mateo-6s93yqcagymy) — San Mateo, CA
- [Robot Science Ops](https://feeny.ai/job/robot-science-ops-generalist-san-mateo-w3g9q4n8eby8) — San Mateo, CA / Somerville, MA
- [Research Scientist: Pretraining](https://feeny.ai/job/research-scientist-pretraining-generalist-san-mateo-7n5nnpe1pwmr) — San Mateo, CA / Somerville, MA