2027 Summer Intern - Research and Development - Machine Learning for Automation

GM•Warren, MI

2d•Hybrid

About The Position

General Motors is seeking a summer intern researcher to contribute to developing and deploying advanced machine-learning methods for intelligent robotic systems in manufacturing. The role applies expertise in machine learning and physical robotics to complex, real-world robotic challenges and supports next-generation automation and flexible production systems. The internship is part of GM’s industrial research organization and focuses on enabling robots to learn, adapt, and perform complex manipulation, processing, and assembly tasks in production environments. The successful intern will contribute to machine-learning techniques and learning-based approaches for robotic tasks, and may contribute to GM’s technical leadership through presentations, intellectual property, technical documentation, and publications. This is a hybrid role based in Warren, Michigan, with an expectation to report onsite at least three times per week. The internship is for current students able to work 40 hours per week during the summer. Start dates are May 24 or June 14, 2027. Eligible selected students will receive a one-time taxable stipend to help facilitate relocation-benefit administration. The role requires a U.S. Person because it is subject to export-control restrictions. GM does not provide immigration-related sponsorship for this position.

Requirements

Pursuing a master’s or Ph.D. in machine learning, physical robotics, computer science, or a closely related field at a leading university.
Strong academic and applied background in machine learning, including experience training policies for grasping, manipulation, contact-rich assembly, or other robotic skills.
Strong programming and system-development skills in Python, C++, and PyTorch or TensorFlow.
Must be a current student.
Must be able to work full-time, 40 hours per week during the summer months.
Must be a U.S. Person (U.S. citizen, U.S. permanent resident, asylee, or refugee) due to export-control restrictions.
Must not require GM immigration sponsorship now or in the future.

Nice To Haves

Experience with dexterous and multi-contact manipulation.
Experience with mobile manipulation in dynamic or unstructured environments.
Experience with learning-based control for contact-rich assembly.
Experience with simulation-to-real transfer and data-efficient learning.
Experience with 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 such as ICRA, RSS, CoRL, or NeurIPS.

Responsibilities

Contribute to the development and application of advanced machine-learning techniques for robotic systems, including reinforcement learning, imitation learning and learning from demonstration, and deep learning for perception, planning, and control.
Apply learning-based approaches to robotic domains such as grasping and dexterous manipulation, mobile manipulation and coordinated motion, and contact-rich assembly and force-sensitive operations.
Contribute to GM’s technical leadership through technical presentations, intellectual property, technical documentation, and publications, as appropriate.