V
AI Engineer (Navigation)
VinMotion
2 hours ago
Full-time
On-site
Cypress, California, United States
About Us: VinMotion is at the forefront of robotics innovation, developing advanced humanoid robots for various applications. We are looking for a skilled AI for Navigation to lead the development, training, and deployment of reinforcement learning (RL) algorithms for humanoid robot navigation.
Key Responsibilities:
Design, implement, test, and deploy localization and navigation algorithms for humanoid robots to navigate autonomously in the environment Train deep learning models for sensor fusion, localization or nagigation that are robust to uncertainty, sensor noise, and partial information Collaborate with other team members to develop toward a full autonomy stack Define performance metrics, test learned policies, and evaluate their effectiveness in real-world scenarios. Requirements:
Bachelor’s or Master’s, PhD’s degree in computer science, AI, Mechatronics, Control Engineering – Automation, or a related field Proficiency in writing production-quality code using PyTorch/Tensorflow/JAX. Familiarity with online and offline RL algorithms, such as PPO and SAC. Familiarity with simulation technologies (e.g., IsaacGym, IsaacLab, Mujoco) Experience in tuning hyperparameters, reward engineering, and optimizing RL training processes. Understand both traditional and machine learning approaches to navigation, and the tradeoffs between various techniques Understanding of the latestest development and framework in RL for manipulation Preferred Qualifications:
Hands-on experience with robot autonomy Experience with deploying real world robot manipulation solutions
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Design, implement, test, and deploy localization and navigation algorithms for humanoid robots to navigate autonomously in the environment Train deep learning models for sensor fusion, localization or nagigation that are robust to uncertainty, sensor noise, and partial information Collaborate with other team members to develop toward a full autonomy stack Define performance metrics, test learned policies, and evaluate their effectiveness in real-world scenarios. Requirements:
Bachelor’s or Master’s, PhD’s degree in computer science, AI, Mechatronics, Control Engineering – Automation, or a related field Proficiency in writing production-quality code using PyTorch/Tensorflow/JAX. Familiarity with online and offline RL algorithms, such as PPO and SAC. Familiarity with simulation technologies (e.g., IsaacGym, IsaacLab, Mujoco) Experience in tuning hyperparameters, reward engineering, and optimizing RL training processes. Understand both traditional and machine learning approaches to navigation, and the tradeoffs between various techniques Understanding of the latestest development and framework in RL for manipulation Preferred Qualifications:
Hands-on experience with robot autonomy Experience with deploying real world robot manipulation solutions
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