About this role
ABOUT THE ROLE
You will build the base intelligence layer for robotics. We train large-scale robot foundation models from massive multimodal datasets spanning video, proprioception, action traces, language, and more. You will design and run the core large-scale training efforts that give our models fundamentally new general capabilities across embodiments, tasks, and environments. You will “live and breathe” all forms of robot data.
You’ll be responsible for:
- Designing and executing large-scale pretraining runs for robot foundation models (transformer- and diffusion-based architectures)
- Defining model architectures, objectives, and training curricula across multimodal robotic data (vision, action, state, language)
- Developing scalable data mixtures and sampling strategies across petabyte-scale datasets
- Guiding data collection operations towards new directions, as well as sourcing new datasets
- Running ablations to understand scaling laws, data quality effects, and architecture tradeoffs
- Collaborating closely with ML Infra and Systems to push cluster utilization, throughput, and reliability
- Turning raw robotic interaction data into generalizable model capabilities
You might thrive in this role if you:
- Have deep experience training large transformer or diffusion models at scale (for generative models e.g. including language models, audio models, or video models)
- Have led or significantly contributed to multi-node, multi-GPU distributed training efforts
- Have worked on scaling laws, optimization dynamics, and large-model failure modes
- Have strong PyTorch fundamentals and comfort debugging at every layer of the stack
- Care about both empirical rigor and raw iteration speed
- Are excited about building general-purpose robot intelligence from first principles
ABOUT GENERALIST
At Generalist, we are on a mission to make general-purpose robots a reality. 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.
You will build the base intelligence layer for robotics. We train large-scale robot foundation models from massive multimodal datasets spanning video, proprioception, action traces, language, and more. You will design and run the core large-scale training efforts that give our models fundamentally new general capabilities across embodiments, tasks, and environments. You will “live and breathe” all forms of robot data.
You’ll be responsible for:
- Designing and executing large-scale pretraining runs for robot foundation models (transformer- and diffusion-based architectures)
- Defining model architectures, objectives, and training curricula across multimodal robotic data (vision, action, state, language)
- Developing scalable data mixtures and sampling strategies across petabyte-scale datasets
- Guiding data collection operations towards new directions, as well as sourcing new datasets
- Running ablations to understand scaling laws, data quality effects, and architecture tradeoffs
- Collaborating closely with ML Infra and Systems to push cluster utilization, throughput, and reliability
- Turning raw robotic interaction data into generalizable model capabilities
You might thrive in this role if you:
- Have deep experience training large transformer or diffusion models at scale (for generative models e.g. including language models, audio models, or video models)
- Have led or significantly contributed to multi-node, multi-GPU distributed training efforts
- Have worked on scaling laws, optimization dynamics, and large-model failure modes
- Have strong PyTorch fundamentals and comfort debugging at every layer of the stack
- Care about both empirical rigor and raw iteration speed
- Are excited about building general-purpose robot intelligence from first principles
ABOUT GENERALIST
At Generalist, we are on a mission to make general-purpose robots a reality. 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.
Tech stack
PyTorch
About Generalist AI
Generalist AI is hiring for the research scientist: pretraining role. NewJob aggregates active openings directly from Generalist AI's applicant tracking system, so this listing is current.
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