Entry-Level – AI and Machine Learning for Automation Researcher

GM•Warren, MI

3d•Hybrid

About The Position

General Motors is seeking a Summer Intern Researcher in Machine Learning for Automation to make meaningful contributions towards driving the development and deployment of advanced learning methods for intelligent robotic systems in manufacturing. This role requires deep technical expertise in machine learning and physical robotics combined with a desire to apply these methods to complex, real-world robotic challenges, supporting the development of next-generation automation and flexible production systems.

Requirements

Pursuing a masters or PhD. in Machine Learning, Physical Robotics, Computer Science, or a closely related field from a leading university.
Must be graduating between August 2026 - June 2027
Able to work fulltime, 40 hours per week
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)

Nice To Haves

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)

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.