VinRobotics, based in Hanoi, Vietnam, is revolutionizing corporate operations with a fleet of autonomous robots addressing labor shortages and challenging tasks. Join our in-office team to help bring our vision of deploying autonomous robots to solve real-world problems.

We are seeking multiple Reinforcement Learning Engineers to lead the development, training, and deployment of reinforcement learning (RL) algorithms for robots. You will play a pivotal role in advancing locomotion and manipulation capabilities by developing scalable
simulation and training infrastructure, ensuring that our robots operate effectively in real-world environments.

Key Responsibilities:
● Develop, train, and deploy reinforcement learning algorithms for robotic motion and
manipulation tasks.
● Build and optimize simulation infrastructure to support large-scale policy training for
general-purpose robots.
● Collaborate with the controls team to integrate learned policies into the robot’s existing
control stack.
● Define performance metrics, test learned policies, and evaluate their effectiveness in
real-world scenarios.

Required Qualifications:
● Proficiency in writing production-quality code using PyTorch/Tensorflow/JAX.
● Familiarity with online and offline RL algorithms, such as PPO and SAC.
● Familiarity with simulation technologies (e.g., Gazebo, IsaacSim, NVIDIA Omniverse
IsaacLab, Mujoco, Bullet)
● Experience in tuning hyperparameters, designing cost functions, and optimizing RL
training processes.
● Knowledge of RL techniques, including domain randomization, curriculum learning, and
reward shaping.
● Familiarity with machine learning evaluation tools like TensorBoard or Weights &
Biases.

Preferred Qualifications:
● Experience in transferring policies learned in simulation to real robot hardware.
● Hands-on experience with massive parallelization training frameworks such as
IsaacGym/IsaacLab
● Hands-on experience in training locomotion policies for quadrupedal or bipedal robots.
● Proven experiences with RL applications in industry or real-world problems.