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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