Research Scientist, RL for Dexterous Manipulation, Atlas

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.

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

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

Nice to have

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

Benefits

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; medical; dental; vision; 401(k); paid time off; annual bonus structure