5-7 years of experience in machine learning, specifically with transformer or diffusion models
Proven track record in multi-node, multi-GPU distributed training
Expertise in scaling laws and optimization dynamics
Strong programming skills in PyTorch, with debugging capabilities across multiple layers
A background in empirical research and iterative model development
Passion for advancing robotics and creating general intelligence from core principles
Responsibilities
Design and execute large-scale pretraining runs for robot foundation models
Define architectures, objectives, and training curricula for multimodal robotic datasets
Develop scalable data mixtures and strategies for handling petabyte-scale datasets
Guide data collection efforts and source new datasets for training
Conduct ablations to examine scaling laws and data quality impacts
Collaborate with ML Infrastructure teams to enhance cluster performance
Transform raw robotic interaction data into versatile model capabilities
Benefits
Collaborative work environment that fosters innovation
Opportunity to contribute to cutting-edge research in robotics
Access to large and diverse multimodal datasets
Participate in a culture valuing empirical rigor and rapid iteration
Chance to revolutionize general-purpose robot intelligence through foundational work
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
