Lead the strategy and execution of the robotics foundation model team, ensuring reliable and scalable AI technology for real-world robots.
About this role
This open-level role develops foundation models connecting vision, language and action for physical robots. Using teleoperation data, the engineer builds generalizable autonomous policies and owns the pipeline from data preparation through robot deployment and optimization.
Work includes multimodal, transformer or VLA model training; collection, preprocessing, annotation and distributed training; integration with controls and hardware; safety and reliability experiments; edge optimization.
Candidates need a machine-learning, computer-science, robotics or related background and professional ML/deep-learning engineering or equivalent research or graduate experience. Hands-on training, fine-tuning or serving of models such as LLMs, vision-language or diffusion models is required, with strong Python, a deep-learning framework, distributed training, ML infrastructure or MLOps, teamwork and problem solving. Professional English is mandatory.
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