🖐️ Dexterous Manipulation Engineer (RL, Imitation Learning, Foundation Models)

Join our team to push the frontier of dexterous manipulation using large-scale reinforcement learning, imitation learning, and generative models. You’ll lead efforts to train general-purpose control policies for high-DOF hands, build robust pipelines for real-world data collection and sim2real transfer, and help shape the future of embodied intelligence. Design and implement scalable training pipelines for dexterous manipulation using RL, IL, and hybrid techniques Leverage demonstrations and human priors to accelerate learning of contact-rich tasks Integrate tactile, visual, and proprioceptive feedback into policy architectures Develop sim environments for dexterous hands and object interaction using MuJoCo or Isaac Transfer policies from simulation to real hardware using sim2real and adaptation strategies Explore diffusion-based policy learning and generative models for action synthesis and planning Collaborate across AI, hardware, and perception teams to build closed-loop manipulation systems Publish or contribute to cutting-edge research while delivering production-quality control stacks