What are the responsibilities and job description for the Engineer position at VinDynamics?
Company Description
At VinDynamics, we design intelligent and accessible humanoid robots to seamlessly assist in everyday life. Backed by Vingroup, Vietnam’s largest technology conglomerate, VinDynamics merges academic expertise and industry experience with a leadership team boasting decades of robotics innovation. From home-assistive robots to advanced security solutions, we aim to make life easier and safer for communities worldwide by bringing cutting-edge robotics technology to every household. Our approach emphasizes safety, affordability, and functionality, ensuring our innovations are both practical and transformative.
Key Responsibilities
- Develop and implement reinforcement learning algorithms specialized for locomotion tasks (e.g., walking, running, climbing, balancing) and manipulation tasks.
- 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.
Qualifications
- Master’s or Ph.D. in Robotics, Computer Science, or a related field, with 2 years of relevant experience.
- Strong background in Reinforcement Learning, including areas such as Deep RL, Policy Gradient methods, Model-based RL, and Imitation Learning.
- Hands-on experience with robotics simulation platforms such as MuJoCo, PyBullet, Isaac Gym, or Gazebo.
- Experience with locomotion, motion control, or physical control systems, such as legged robots, drones, exoskeletons, or robotic manipulators.
- Experience with sim-to-real transfer, domain randomization, or system identification for robotic systems.
- Proficiency in Python and/or C , with experience using machine learning frameworks such as PyTorch, TensorFlow, or JAX.
- Strong analytical and debugging skills for physical robotic systems, with the ability to identify stability and performance bottlenecks.
- Familiarity with sensor fusion, feedback control, and proprioceptive sensing.