What To Expect 

This position is expected to start in-person around May/June 2025 and continue through the entire Summer (i.e. through Aug/Sep 2025). We ask for a minimum of 10 weeks. Interns should expect to work Monday-Friday, up to 40 hours per week, typically between 9am-5pm. Specific team norms around working hours will be communicated by your manager. Please consider before applying. 

Requirements

• MUST be an MS or PhD student enrolled in an accredited program during the internship term. (New graduates not enrolled in an accredited program for Fall 2025 are ineligible.)
• Pursuing a degree in Computer Science, Robotics, Electrical/Mechanical/Aerospace Engineering, or a related field.
•  Experience in one or more of the following areas:
  • Machine learning, particularly in reinforcement learning, large language models (LLMs), task planning, or skill chaining for autonomous robotics.
  • Software and algorithm development for control, planning, perception, or autonomy for humanoid robotics.
• Proficient in C++ and Python in a Linux development environment, with prior demonstrated ability to:
  • Translate complex theoretical concepts and algorithms into practical, efficient, and bug-free software implementations.
  • Perform effectively in a fast-paced software development team using CI tools like Git, adhering to rigorous documentation and testing standards.
• Familiar with modern software tools/libraries relevant to robotics autonomy and task planning, including:
  • Robotics frameworks such as ROS, Ignition Gazebo, Pinocchio, MuJuCo, OMPL, MoveIt, etc.
  • Machine learning frameworks like PyTorch or TensorFlow, with a focus on task planning and control.
  • Behavior tree libraries (e.g., BehaviorTree.CPP, BT.CPP) and relevant tools for task sequencing and autonomous behavior design
• Strong theoretical understanding in a significant subset of Dynamics, control systems, estimation, reinforcement learning, behavior trees, or natural language processing as it relates to task planning in autonomous robots.
• Excellent communication and documentation skills to support clear presentation of complex AI-driven autonomy methods.
• GPA of 3.0 or above.
• Experience integrating LLMs and task planning to support dynamic instruction parsing, context-driven behavior modifications, and real-time task sequencing.
• Experience with behavior tree frameworks, leveraging them for reactive and adaptive planning in real-world, dynamic environments.

Nice to have

• Experience developing and debugging real-time, multithreaded software systems
• Experience implementing model-based feedback control of articulated robots and troubleshooting hardware
• Experience with modeling dynamic systems, system identification, and optimization solvers
• Experience with embedded systems and firmware development
• Familiarity with RTPreempt and Linux kernel configuration
• Experience with EtherCAT

Qualities

• Creativity
• Humility
• Integrity
• Passion
• Curiosity
• Analytical
• Disciplined