Machine Learning Engineer

Key Responsibilities

Develop state-of-the-art reinforcement learning algorithms for robotic control, planning, and decision-making.

Build and maintain simulation environments (e.g., Isaac Gym, Mujoco, Gazebo, PyBullet) for scalable RL training.

Train RL agents using deep learning frameworks (PyTorch / TensorFlow) and optimize them for sim-to-real transfer.

Collaborate with robotics and controls engineers to integrate RL policies on real robotic hardware (arm manipulators, humanoids, etc.)

Implement robust RL pipelines including data collection, reward shaping, distributed training, and evaluation.

Perform benchmarking, diagnostics, and troubleshooting of RL algorithms under various operational conditions.

Research and apply methods such as offline RL, hierarchical RL, imitation learning, model-based RL , or multi-agent RL .

Optimize for safety, performance, and real-time constraints in robotic applications.

Document technical findings, results, and best practices.

Required Qualifications

Bachelor’s or Master’s degree in Computer Science, Robotics, Machine Learning, Electrical Engineering , or related field.

Strong experience with Reinforcement Learning and deep learning (DDPG, PPO, SAC, TD3, A3C, etc.).

Proficiency in Python and RL / ML frameworks (PyTorch preferred).

Hands-on experience with robotics simulation tools (Isaac Gym, Mujoco, PyBullet, Gazebo, or others).

Solid understanding of kinematics, control theory, or robot dynamics.

Experience integrating algorithms with real robotic hardware (ROS / ROS2 experience strongly preferred).

Strong debugging skills and ability to analyze complex system behaviors.