Research Scientist: Pretraining

• 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

• 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

Offers Equity