Reinforcement-Learning Locomotion Engineer

VinDynamics; Reno (NV); On-site; USD 90,000 - 120,000; Full time

Job summary

VinDynamics is seeking a talented professional to develop and implement reinforcement learning algorithms specifically for locomotion tasks. This role involves designing high-fidelity simulation environments and conducting sim-to-real transfers that address complex challenges in robotics.

Candidates should have a solid background in reinforcement learning, experience with simulation platforms like MuJoCo or PyBullet, and proficiency in programming with Python or C++. Join us in redefining locomotion through innovative technology.

Qualifications

• Solid background in Reinforcement Learning including Deep RL and Policy Gradient.
• Hands-on experience with simulation platforms like MuJoCo or PyBullet.

Responsibilities

• Develop reinforcement learning algorithms for locomotion tasks.
• Design and optimize high-fidelity simulation environments.
• Conduct sim-to-real transfer addressing robustness and domain randomization.

Skills

Reinforcement Learning

Simulation platforms (MuJoCo, PyBullet)

Python and/or C++ proficiency

Strong analytical skills

Job description

VinDynamics is seeking a talented professional to develop and implement reinforcement learning algorithms specifically for locomotion tasks. This role involves designing high-fidelity simulation environments and conducting sim-to-real transfers that address complex challenges in robotics.

Candidates should have a solid background in reinforcement learning, experience with simulation platforms like MuJoCo or PyBullet, and proficiency in programming with Python or C++. Join us in redefining locomotion through innovative technology.