Research Scientist, RL for Dexterous Manipulation, Atlas

Job description

Are you passionate about using reinforcement learning to solve dexterous manipulation tasks? As an RL Research Scientist , you'll lead research projects on visual sim-to-real transfer and post-training of Vision-Language-Action (VLA) models. Your job is to turn unlabeled data from simulation or the real-world into robust real-world manipulation skills. You should push the frontier of what bimanual and multi-fingered systems can do in unstructured environments.

Job responsibilities

Develop novel algorithms for visual sim-to-real transfer with photorealistic rendering
Design post-training recipes that improve pretrained VLA models on manipulation tasks
Research reward modeling, and offline-to-online RL for large multimodal policies
Close the sim-2-real gap through tactile sensing, vision, and system identification
Train policies that generalize across objects, scenes, and embodiments

Job requirements

PhD, in ML, Robotics, or a related field or a MS with 3+ years of experience
Track record of first-author publications at top venues (CoRL, RSS, ICLR, NeuRIPS)
Demonstrated experience training policies for dexterous or contact-rich manipulation
Hands-on experience with VLA models, diffusion policies, or large behavior models
Proficient in PyTorch and/or JAX, with experience training models at scale
Strong software fundamentals and the ability to ship research code that runs reliably

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Research Scientist, RL for Dexterous Manipulation, Atlas

Job description

Job responsibilities

Job requirements

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