Career

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 

• Publication in related field