Research Scientist: Pretraining

Generalist; San Francisco (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

Generalist is seeking a robotics specialist to build the base intelligence layer for robotics models in San Francisco. The role involves training large-scale transformer models, designing pretraining runs, and collaborating on data collection operations.

Ideal candidates will have extensive experience in distributed training and strong PyTorch skills. As an equal opportunity employer, Generalist values diversity and is committed to creating an inclusive environment.

Qualifications

• Deep experience training large transformer or diffusion models at scale.
• Experience with multi-node, multi-GPU distributed training.
• Knowledge of scaling laws and optimization dynamics.

Responsibilities

• Design and execute large-scale pretraining for robot foundation models.
• Define model architectures and training curricula.
• Collaborate with ML Infra to enhance cluster utilization.

Skills

Large transformer model training

Distributed training systems

Data collection and analysis

PyTorch debugging

Job description

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, audio, 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

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.