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Embodied AI Algorithm Engineer/Expert (Reinforcement Learning)

Beijing
Full-time
Reinforcement Learning/Robotics Control Direction
Relevant background

You will be responsible

  • Design and implement reinforcement learning/imitation learning algorithms for robot motion control and operation problems
  • Build training tasks and pipelines in a simulation environment, and promote the implementation of Sim2Real
  • Promote the deployment, debugging, and performance optimization of algorithms on real robots
  • Collaborate with the hardware/system team to achieve stable and reliable closed-loop control

We hope you have

  • a background in computer science / robotics / automation or related fields
  • a solid foundation in reinforcement learning or control, with practical project experience
  • familiarity with Python, and frameworks such as PyTorch / JAX
  • familiarity with simulators such as MuJoCo / Isaac Sim
  • familiarity with common reinforcement learning algorithms (such as PPO) and practical application experience
  • an understanding of robotics fundamentals (kinematics / dynamics / control)

Preferred Qualifications

  • experience with humanoid robot motion control and robotic manipulation
  • Experience with Sim2Real, online RL, or closed-loop learning from on-robot data
  • Experience with edge deployment and inference optimization (Jetson / TensorRT, etc.)
  • Experience with top conference papers or open source projects

Our way of working

  • Emphasize real closed-loop: algorithms should not only be effective in simulation, but also run stably on real robots.
  • Strong engineering orientation: connect the full chain from data → model → deployment, rather than just optimizing a single algorithm.
  • Rapid iteration: Move frequently between simulation and physical robots to validate and refine solutions quickly.
  • Strong collaboration: algorithms, hardware, and systems closely work together to solve complex embodied problems.

Interested in This Role?

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