Description:
At VinDynamics, we build home assistive robots that will help billions of people and families across the world. We are seeking talented and self-driven Reinforcement Learning engineers to join our stellar team. You will work on cutting-edge policy learning algorithms that allow the robot to walk, run, balance, and recover from disturbances in real-world environments.
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.
