Senior Robotics Software Engineer

ABOUT VINDYNAMICS:At VinDynamics, we design safe, affordable, and intelligent humanoid robots to assist in everyday life — robots for everyone. Backed by Vingroup, Vietnam’s leading technology conglomerate, we are on a mission to make advanced robotics accessible, reliable, and beneficial for billions of people worldwide. By combining cutting-edge AI, world-class engineering, and human-centered design, we aim to seamlessly integrate robots into daily life — enhancing safety, productivity, and happiness at home and beyond.I. OVERVIEWPosition: Senior Reinforcement Learning Engineer (Humanoid Robot)Division - Department: R&D Division Report to: Head of MobilityLocation: Reno, NevadaII. REQUIREMENTSRelevant education and experienceM.S. or Ph.D. in Robotics, Computer Science, Electrical/Mechanical Engineering, or a related fieldSolid understanding and experience of RL algorithms (PPO, SAC, TD3, A3C, etc.) and policy optimizationHands-on experience with simulation platforms such as Isaac Gym/Isaac Lab, MuJoCo, PyBullet, or Gazebo.Experience integrating learned policies with real robots (e.g., quadrupeds, manipulators, or mobile arms)Preferred QualificationsExperience with locomotion, motion control, or physical control systems (e.g., legged robots, drones, exoskeletons, robotic arms).Experience in sim-to-real transfer, domain randomization, or system identification in robotics.Proficiency in Python and/or C++, and familiarity with ML frameworks such as PyTorch, TensorFlow, or JAX.Strong analytical and debugging skills for physical systems; ability to identify stability and performance bottlenecks.Familiarity with sensor fusion, feedback control, and proprioceptive sensing.Personality/ AttitudeStrong interpersonal, organizational and leadership skillsProactive, dedicated, business-oriented, responsible and willing to learnGood communication skills, creative problem-solving skills and attention to detail.III. JOB DESCRIPTIONDevelop and implement reinforcement learning algorithms specialized for locomotion tasks (e.g., walking, running, climbing, balancing) and loco-manipulation tasks (e.g., walking while carrying or manipulating objects).Design, integrate, and optimize high-fidelity simulation environments for safe and efficient policy training.Conduct sim-to-real transfer by addressing robustness, domain randomization, and system identification challenges.Incorporate perception, sensor feedback, and proprioception into RL agents to enable adaptive and reactive motion.Evaluate and benchmark locomotion policies under diverse real-world conditions (e.g., terrain variation, disturbances, slopes, payloads, and friction).Work on reward design, stability, sample efficiency, and safety-constrained learning.Write clean, maintainable, and well-documented code, ensuring reproducibility and version control for experiments and policies.