Research Scientist, RL for Dexterous Manipulation, Atlas

Boston Dynamics•Waltham, MA

20h•Onsite

About The Position

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.

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 Haves

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

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