Build and maintain simulation scenes in Isaac Lab for robot manipulation tasks, including cable routing, cable insertion, and pick-and-place.

Implement, run, and analyze experiments to evaluate the effectiveness of different robotic policies in simulation.

Support training workflows for robotic learning approaches, including hyperparameter tuning, fine-tuning, and performance benchmarking.

Work closely with robotics and research engineers to investigate new task setups, validate approaches, and improve simulation fidelity and task success.

Design, execute, and document experiments to measure performance, robustness, and failure modes.

Analyze results and communicate findings through clear visualizations, summaries, and technical discussions.

Pursuing a BS/MS in Robotics, Mechatronics, Mechanical Engineering, Electrical Engineering, Computer Science, Aerospace Engineering, or a related field.

Experience with Isaac Lab, Isaac Sim, Omniverse, or similar robotic simulation tools.

Experience with robotics, reinforcement learning, imitation learning, or control systems through coursework, research, or personal projects.

Ability to interpret experimental data, analyze results, and communicate insights clearly.

Strong problem-solving skills and attention to detail in experimental setup and evaluation.

Examples of personal, academic, or professional projects that demonstrate hands-on robotics, simulation, or machine learning experience.

Familiarity with robot manipulation, motion planning, or robot learning for contact-rich tasks.

Experience training policies and tuning hyperparameters for reinforcement learning or other learning-based methods.

Knowledge of robot kinematics, dynamics, control, and perception.

Strong programming skills in Python`

Experience working with robotic arms and manipulation tasks in either simulation or real hardware.

Experience with experiment automation, logging, and visualization tools for ML or robotics workflows.