VinMotion

Career

Reinforcement Learning Engineers

  • Ha Noi
  • Fulltime

Key Responsibilities:

  • Design, train, and deploy reinforcement learning (RL) algorithms for various robotic motion tasks

  • Develop and optimize simulation infrastructure to enable large-scale policy training for general-purpose robots.

  • Collaborate with the other teams to integrate learned RL policies into the robot’s existing control architecture.

  • Define evaluation metrics, conduct performance testing, and assess policy effectiveness.

Requirements:

  • Strong proficiency in developing production-grade code using frameworks such as PyTorch, TensorFlow, or JAX.

  • Solid understanding of both online and offline RL algorithms, including PPO and SAC.

  • Experience with simulation platforms such as IsaacGym, MuJoCo, Gazebo, IsaacSim, NVIDIA Omniverse IsaacLab, or Bullet.

  • Skilled in hyperparameter tuning, cost function design, and overall, RL training optimization.

  • Familiarity with advanced RL techniques like domain randomization, curriculum learning, and reward shaping.

  • Proficient in using machine learning evaluation tools such as TensorBoard or Weights & Biases.

Preferred Qualifications:

  • Experience in deploying learned policies from simulation to real robotic hardware systems.

  • Practical experience with large-scale parallel training frameworks such as IsaacGym.

  • Background in training locomotion policies for bipedal or quadrupedal robots.

  • Demonstrated success in applying RL to real-world problems or in industrial settings.

Benefits:

Competitive salary and benefits package (Open to salary negotiations).  

Opportunities for professional development and career growth.  

Flexible work arrangements.  

A collaborative and innovative work environment.  

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