Research Scientist, RL for Dexterous Manipulation, Atlas
- 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
- 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
- Deployed vision-based manipulation policies on physical robots
- Deep knowledge of sim-to-real transfer techniques and photorealistic rendering
- Built training pipelines that combine RL, imitation learning, and large-scale pretraining
- Experience fine-tuning foundation models with RLHF, DPO, GRPO, or related methods
- Familiarity with tactile sensing, multi-fingered hands, or bimanual coordination
- Direct impact on the policies powering our humanoid robots in the real world
- Access to a world-class fleet of robots, simulation infrastructure, and compute
- The chance to define what's possible for dexterous manipulation at scale
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