Research Scientist: Pretraining

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.

If you have worked on pretraining large-scale ML systems or generative foundation models e.g. multimodal, language, audio, etc. (not only robotics), then this may be the role for you.

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 worked on large-scale ML systems with senior/staff (L6+) experience.
• 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