---
title: 'Applied Scientist at Generalist'
canonical: 'https://feeny.ai/job/applied-scientist-generalist-boston-2q1hybp99n34'
type: 'job'
last_seen: '2026-11-11'
---

# Applied Scientist at Generalist

- **Company:** Generalist
- **Location:** Boston, MA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-11-04
- **Last confirmed live:** 2026-11-11
- **Apply:** https://jobs.ashbyhq.com/generalist/e3460ad7-8044-4522-aedd-009926900b14

## 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 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

Applied Scientists own the mastery of new frontier model capabilities. This includes developing post-training recipes for learning a wide range of dexterous tasks — from pushing the frontiers of science lab automation, to generalizing manufacturing and warehouse logistics, to designing open-ended evaluations that challenge what the models can do, and more. You are responsible for hill-climbing broad sets of manipulation capabilities until they are robust, general, and reusable across multiple applications. This role is a hybrid of research, engineering, and operations, and we expect excellence in all three areas. This is an empirical, model science-first role. You will define what "good" looks like for a capability, build the evaluations that measure it, run experiments across model variants and data recipes, analyze the results, and drive the post-training loop. You will leverage post-training, data, task design, and rigorous experimentation, to compound model capabilities: anything mastered for one application can become a strength of the foundation model for entire industries.

You'll be responsible for:

- Owning end-to-end improvement of target capabilities, from baseline performance to mastery
- Designing task suites and evaluation protocols that faithfully capture what research, science partners, and customers need, and that generalize beyond any single deployment
- Coordinating data collection, training and fine-tuning models, sweeping data mixtures and configurations, and orchestrating large-scale evaluations
- Running rigorous experimental analysis across model variants — isolating variables, quantifying statistical confidence, and diagnosing failure modes from rollout data
- Using the stack in new ways rather than redesigning it — and when a capability is fundamentally restricted, you'll work with the pretraining and post-training teams to improve the model

You might thrive in this role if you:

- Are comfortable with data analysis at scale: slicing roll out logs, computing metrics, visualizing results, and investigating what happened and why
- Care deeply about evaluation, benchmarking, and failure analysis — and are continuously diligent in the face of seemingly repetitive, but subtly changing task evaluations
- Enjoy rapid iteration with real-world feedback loops, and are willing to get hands-on with robots and physical task setups when the experiment demands it
- Are motivated by making capabilities work for real applications
- Can communicate findings clearly to both research teams and partner-facing teams
- Have a strong background in machine learning and/or robot learning (e.g. a PhD in a relevant area)

## 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 Assistant](https://feeny.ai/job/research-assistant-generalist-boston-ram5bn9jthxf) — Boston, MA
- [Research Assistant](https://feeny.ai/job/research-assistant-generalist-san-mateo-0ty8sjcr4exb) — San Mateo, CA
- [Partnerships Lead](https://feeny.ai/job/partnerships-lead-generalist-san-mateo-4e85tb1qjae7) — San Mateo, CA
- [Strategic Deals Lead](https://feeny.ai/job/strategic-deals-lead-generalist-boston-zhpm43fv2bn7) — Boston, MA
- [Software Engineer: Robotics Controls](https://feeny.ai/job/software-engineer-robotics-controls-generalist-boston-ve9r48szppa1) — Boston, MA
- [Software Engineer: Product](https://feeny.ai/job/software-engineer-product-generalist-boston-0tfwre84cxqh) — Boston, MA
- [Software Engineer: ML Optimization](https://feeny.ai/job/software-engineer-ml-optimization-generalist-boston-ggcpct8zdxjk) — Boston, MA
- [Software Engineer: ML Infra](https://feeny.ai/job/software-engineer-ml-infra-generalist-boston-f35tk53466r6) — Boston, MA
- [Software Engineer: Infrastructure](https://feeny.ai/job/software-engineer-infrastructure-generalist-boston-bsbmsdy3wv45) — Boston, MA
- [Software Engineer: Frontend](https://feeny.ai/job/software-engineer-frontend-generalist-boston-fax429cn12fb) — Boston, MA