Humanoid 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

• Develop and implement reinforcement learning algorithms specialized for locomotion tasks (e.g., walking, running, climbing, balancing).
• Design, integrate, and optimize high-fidelity simulation environments for safe and efficient policy training.
• Conduct sim-to-real transfer by addressing robustness, domain randomization, and system identification challenges.
• Incorporate perception, sensor feedback, and proprioception into RL agents to enable adaptive and reactive motion.
• Evaluate and benchmark locomotion policies under diverse real‑world conditions (e.g., terrain variation, disturbances, slopes, payloads, and friction).
• Work on reward design, stability, sample efficiency, and safety‑constrained learning.
• Write clean, maintainable, and well‑documented code, ensuring reproducibility and version control for experiments and policies.

Requirements

• Solid background in Reinforcement Learning (Deep RL, Policy Gradient, Model‑based RL, Imitation Learning, etc.).
• Hands‑on experience with simulation platforms such as MuJoCo, PyBullet, Isaac Gym, or Gazebo.

Preferred Qualifications

• Experience with locomotion, motion control, or physical control systems (e.g., legged robots, drones, exoskeletons, robotic arms).
• Experience in sim‑to‑real transfer, domain randomization, or system identification in robotics.
• Proficiency in Python and/or C++, and familiarity with ML frameworks such as PyTorch, TensorFlow, or JAX.
• Strong analytical and debugging skills for physical systems; ability to identify stability and performance bottlenecks.
• Familiarity with sensor fusion, feedback control, and proprioceptive sensing.