Key Responsibilities

• Develop and implement reinforcement learning algorithms specialized for locomotion tasks (e.g., walking, running, climbing, balancing).
• 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.

Requirements

• Solid background in Reinforcement Learning (Deep RL, Policy Gradient, Model-based RL, Imitation Learning, etc.).
• Hands-on experience with simulation platforms such as MuJoCo, PyBullet, Isaac Gym, or Gazebo.

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.

Location:

• Hanoi Vietnam
• Reno, Nevada, US (coming soon)

Compensation:

VinDynamics offers a competitive base, full benefits and other incentives. Flexible time-off policy. Focus on output and ability to work with an interdisciplinary team.