Simulation Software Engineer - Intern

Our mission is to solve the most important and fundamental challenges in AI and Robotics to enable future generations of intelligent machines that will help us all live better lives.

Robotics Software Interns/Coops will work cross-functionally to improve robot software at the institute and around the world.  If you have a passion for developing technology for robots and use it to advance their capabilities and usefulness, you will want to join us!

As a Simulation Software Intern, you will have the opportunity to work closely with experienced simulation software engineers, contribute to ongoing projects, and gain hands-on experience in developing software solutions for our cutting-edge robotic systems. Along with this you will also work on a fun demo project that highlights your contributions over the internship. This unique opportunity will provide you with valuable insights into the practical applications of your learning and give you a chance to showcase your work.

• Work under the guidance of our simulation software engineers to produce meaningful contributions to our robotic systems
• Work collaboratively with research teams to gather requirements, feedback, and test your solutions
• Create and support open source robotics software
• Promote quality and reliability through regular code reviews

• 2+ years experience delivering robust code (internships, research positions and open source projects included)
• BS or MS in computer science, robotics, physics, electrical/mechanical/aerospace engineering, or a related field
• Experience with simulation for robotics - Isaac Sim, Isaac Lab, Habitat, Drake, Mujoco, Gazebo, CoppeliaSim, etc.
• Experience with robot sensors (IMU, cameras, depth cameras, etc)Experience working with robots, controls, kinematics, machine learning, etc.
• Knowledge of Omniverse tools such as Isaac, Orbit, Warp, Replicator, etc.
• Strong programming skills in Python or C++Experience with git, issue tracking, CI/CD, and modern software engineering practices
• Strong troubleshooting skills

• Familiar with the gym-type environments for Reinforcement Learning (RL)
• Experience with simulation development in conjunction with ROS/ROS2
• Familiar with robot perception (camera models, etc)
• Experience with synthetic data generation and domain randomization
• Experience with system identification, modeling real world imperfections, and closing sim-to-real gap