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. OVERVIEW

• Position: Senior Reinforcement Learning Engineer (Humanoid Robot)
• Division - Department: R&D Division
• Report to: Head of Mobility
• Location: Reno, Nevada

II. REQUIREMENTS

Relevant education and experience

• M.S. or Ph.D. in Robotics, Computer Science, Electrical/Mechanical Engineering, or a related field
• Solid understanding and experience of RL algorithms (PPO, SAC, TD3, A3C, etc.) and policy optimization
• Hands-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 Qualifications

• Experience 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/ Attitude

• Strong interpersonal, organizational and leadership skills
• Proactive, dedicated, business-oriented, responsible and willing to learn
• Good communication skills, creative problem-solving skills and attention to detail.

III. JOB DESCRIPTION

• Develop 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.