General Motors
2027 Summer Intern - Research and Development - Machine Learning for Automation
Warren, Michigan, United States of America · Hybrid · Internship · Posted Oct 12, 2026
- Education
- Pursuit of a Master’s or PhD in Machine Learning, Physical Robotics, Computer Science, or a closely related field.
- Visa sponsorship
- GM does not provide immigration-related sponsorship now or in the future. The role requires the successful candidate to be a U.S. Person (U.S. citizen, U.S. permanent resident, asylee, or refugee).
About the job
Develop and apply machine-learning methods for robotic systems performing manipulation, processing, and assembly in real-world manufacturing environments.
What you'll do
- Develop and apply machine-learning techniques for robotics, including reinforcement, imitation, and deep learning.
- Apply learning-based approaches to robotic tasks such as grasping, mobile manipulation, and contact-rich assembly.
- Contribute to technical presentations, intellectual property, documentation, and publications.
What they ask for
- Pursuing a Master’s or PhD in Machine Learning, Physical Robotics, Computer Science, or a closely related field; must be a current student.
- Strong academic and applied machine-learning background, including experience training policies for robotic skills.
- Strong programming and system-development skills with Python, C++, and PyTorch/TensorFlow.
- Able to work full-time, 40 hours per week during the summer.
Nice to have
- Experience with dexterous or mobile manipulation, contact-rich assembly, simulation-to-real transfer, or data-efficient learning.
- Experience with foundation-model-enabled robotics, industrial or automotive manufacturing, or publications in leading conferences.
Tools
Python, C++, PyTorch, TensorFlow
AI focus
Develop and apply machine learning for robotic perception, planning, control, manipulation, and assembly; preferred work includes foundation-model-enabled robotics and generative AI methods.
Model-written summary · the original posting is authoritative