5-7 years of experience in large model training (transformer or diffusion)
Demonstrated leadership in multi-node, multi-GPU distributed training
Expertise in scaling laws and optimizations
Proficient in PyTorch with troubleshooting at all stack layers
Strong emphasis on empirical rigor and rapid iterations
Passion for developing foundational robotic intelligence
Responsibilities
Design and execute large-scale pretraining runs for robot models
Define model architectures and training goals using multimodal data
Develop scalable datasets and strategic sampling methods
Lead data collection initiatives and identify new datasets
Conduct ablation studies to analyze scaling laws and data quality
Collaborate with ML infrastructure teams to enhance system performance
Convert raw robotic data into actionable model capabilities
Benefits
Flexible work hours
Opportunity to work at the forefront of robotics and AI
Collaborative and innovative team environment
Access to cutting-edge technology and resources
Professional development opportunities
Full 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 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
