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You will build large multi-modal generative “world models” that predict future sensor inputs and robot actions based on past observations. These foundational models enable robots to understand and operate effectively in unstructured, real-world environments. You’ll work across data pipelines, model architectures, and deployment to drive improvements in robot autonomy. This role combines cutting-edge research with hands-on product development to push the limits of robot intelligence.
Job Responsibility:
Build full-stack systems across data engineering, model architecture, and product delivery
Develop high-throughput data loaders for large-scale, multi-modal robot datasets
Implement tokenizers and transformers designed for web-scale robotic data
Improve robot autonomy by advancing world model architectures
Predict robot performance in the real world based on pre-training metrics such as log loss
Requirements:
Strong experience with Python and related tools such as Bazel
Proficiency with PyTorch or equivalent deep learning frameworks
Familiarity with simulation platforms such as Isaac Sim or MuJoCo
Experience working with multi-modal generative models combining video, audio, text, and action prediction
Ability to design and optimize large-scale data pipelines for training
Understanding of scaling laws and performance metrics for foundation models
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