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

General Motors LLC; United States; Hybrid; USD 28.000 - 41.000; Vollzeit

Zusammenfassung

General Motors is inviting a Summer Intern Researcher in Machine Learning for Automation to contribute to the development and deployment of advanced learning methods for intelligent robotic systems in manufacturing. The role demands deep ML and robotics expertise and a passion for applying methods to real-world production challenges at GM's industrial research organization.

The internship supports the creation of learning-capable robotic systems for manipulation, processing, and assembly tasks

Qualifikationen

• Pursuit of an advanced degree (Masters or PhD) in one of the following areas: 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

Aufgaben

• 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, which may include: 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

Kenntnisse

Python

C++

Machine Learning

Robotics

PyTorch/TensorFlow

Ausbildung

Master's or PhD candidate in ML/Robotics/CS

Tools

PyTorch

TensorFlow

ROS

Jobbeschreibung

Job Description

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. This summer internship will provide an opportunity to make meaningful contributions as part of a leading industrial research organization toward the future of intelligent automation at GM by enabling robotic systems capable of learning, adapting, and performing complex manipulation, processing, and assembly tasks in real-world production environments, unlocking new levels of flexibility, efficiency, and capability in manufacturing.

What You'll Do

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, which may include:

• 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.

Required Qualifications

• Pursuit of an advanced degree (Masters or PhD) in one of the following areas: 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

Preferred Qualifications

• 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)

GM does not provide immigration-related sponsorship for this role. Do not apply for this role if you will need GM immigration sponsorship now or in the future. This includes direct company sponsorship, entry of GM as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc). The position is subject to export control restrictions and requires the successful candidate to be a U.S. Person (U.S. citizen, U.S. permanent resident, asylee or refugee).

Work Arrangement

Hybrid: This role is categorized as hybrid. This means the successful candidate is expected to report to Warren, MI three times per week, at minimum [or other frequency dictated by the business]. GM will provide a one-time lump sum taxable stipend payment to eligible students selected for the 2027 Student Program - to help facilitate administration of relocation benefits, please apply using the permanent address you would move from.

Start dates for this internship role are May 24th & June 14th of 2027.

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