Research Scientist: Pretraining

Generalist AI; San Mateo, Somerville (CA, MA); On-site; USD 180,000 - 240,000; Full time

Job summary

Generalist is developing the base intelligence layer for robotics by training large-scale robot foundation models from massive multimodal datasets (video, proprioception, action traces, language, and more).

You will design and run the core training efforts that give our models new general capabilities across embodiments, tasks and environments, turning raw robotic interaction data into practical, generalizable intelligence.

Qualifications

• Deep experience training large transformer or diffusion models at scale.
• Led or significantly contributed to multi-node, multi-GPU distributed training efforts.
• Worked on scaling laws, optimization dynamics, and large-model failure modes.
• Strong PyTorch fundamentals and debugging across stack.
• Commitment to empirical rigor and rapid iteration.
• Eagerness to build general-purpose robot intelligence from first principles.

Responsibilities

• Designing and executing large-scale pretraining runs for robot foundation models (transformer- and diffusion-based).
• Defining model architectures, objectives, and training curricula across multimodal robotic data.
• Developing scalable data mixtures and sampling strategies across petabyte-scale datasets.
• Guiding data collection operations towards new directions and sourcing new datasets.
• Running ablations to understand scaling laws and data quality effects.
• Collaborating with ML Infra and Systems to push cluster utilization, throughput and reliability.
• Turning raw robotic interaction data into generalizable model capabilities.

Skills

Transformer models

Diffusion models

Distributed training

PyTorch

Model scaling

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, RT-2, Gemini Robotics), launched and scaled ChatGPT and GPT-4 to hundreds of millions of users, engineered the foundations of autonomous driving, built next-generation robots (Atlas, Spot, Stretch) and pushed the limits of what they can do (from parkour to manipulation, and testing robustness).

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

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