Summer Intern - Research and Development - Machine Learning for Automation

About This Role

This summer internship offers the opportunity to contribute to the development and deployment of advanced learning methods for intelligent robotic systems in manufacturing. The role suits a researcher with deep technical expertise in machine learning and physical robotics, eager to apply these methods to complex, real-world robotic challenges and support the development of next-generation automation and flexible production systems.

Responsibilities

• Make meaningful contributions in the development and application of advanced machine learning techniques for robotic systems, which may include:
• Reinforcement learning (RL)
• Imitation learning and learning from demonstration
• Deep learning methods for perception, planning, and control
• Make meaningful contributions in the application of learning-based approaches to challenging robotic domains, including:
• Grasping and dexterous manipulation
• Mobile manipulation and coordinated motion
• Contact-rich assembly and force-sensitive operations
• Make meaningful contributions to GM’s technical leadership as appropriate through technical presentations, intellectual property, technical documentation, and publication

Requirements

• Pursuit of an advanced degree (Masters or PhD) in Machine Learning, Physical Robotics, Computer Science, or a closely related field from a leading university
• Strong academic and applied background in machine learning, with experience training policies for grasping, manipulation, contact-rich assembly, or other robotic skills
• Strong programming and system development skills (Python, C++, PyTorch/TensorFlow)
• Must be a current student
• Able to work fulltime, 40 hours per week during the summer months
• Must be a U.S. Person (U.S. citizen, U.S. permanent resident, asylee or refugee)

Nice to have:

• Experience with:
• Dexterous and multi-contact manipulation
• Mobile manipulation in dynamic or unstructured environments
• Learning-based control for contact-rich assembly
• Simulation-to-real transfer and data-efficient learning
• Foundation-model-enabled robotics, including vision-language-action models, transformer-based control architectures, world models, and emerging generative AI methods for robot planning and execution
• Experience in industrial or automotive manufacturing environments
• Publication record in leading conferences (e.g., ICRA, RSS, CoRL, NeurIPS)

Benefits

• One-time lump sum taxable stipend payment to eligible students selected for the 2027 Student Program

Important Details