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You will build large multi-modal generative “world models” that predict future sensor inputs and actions of the robot from its past observations. These foundational models are meant to enable the robot to understand and act in unstructured, real-world environments. You will work across data pipelines, model architecture, and product deployment to drive improvements in robot autonomy.
Job Responsibility:
Full-stack engineering: data engineering, model architecture design, and delivering polished products
Develop high-throughput data loaders for large multi-modal datasets
Implement tokenizers and transformers tailored for web-scale robot data
Translate improvements in world model architectures into improvements in robot autonomy
Predict how real-world robot performance scales with pre-training metrics (e.g., log loss)
Requirements:
Strong experience with Python (and related tooling such as Bazel)
Proficiency with PyTorch or equivalent deep learning frameworks
Familiarity with simulation platforms (e.g., Isaac Sim, MuJoCo)
Experience building or working with multi-modal generative models combining video, audio, text, and action prediction
Ability to design and optimize large-scale data pipelines and loaders for training
Understanding of scaling laws and metrics for foundation models
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