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

Aimlroles; Warren (MI); Hybrid; USD 25,000 - 39,000; Full time

Benefits offered by this job

Hybrid work arrangement

Relocation stipend for eligible shifts

Job summary

General Motors is seeking a Summer Intern Researcher in Machine Learning for Automation to advance learning methods for intelligent robotic systems in manufacturing. The role supports development and deployment of autonomous manipulation in real-world production environments, aiming to increase flexibility and efficiency.

The internship requires pursuing an advanced degree in ML or robotics, strong ML background, and proficiency in Python, C++, PyTorch, or TensorFlow.

Qualifications

• Pursuit of an advanced degree (Masters or PhD) in ML, Physical Robotics, CS or a closely related field from a leading university.
• Strong academic and applied background in machine learning with experience training policies for grasping, manipulation or related robotic skills.
• Strong programming and system development skills (Python, C++, PyTorch/TensorFlow).
• Must be a current student.

Responsibilities

• Develop and apply advanced machine learning techniques for robotic systems (RL, imitation learning, deep learning for perception, planning, control).
• Address grasping, dexterous and mobile manipulation, contact-rich assembly and force-sensitive operations.
• Contribute to GM's technical leadership through presentations, documentation and publications.

Skills

Machine learning

Robotics

Current student

Education

Master's or PhD in ML/Robotics/CS

Tools

Python

C++

PyTorch

TensorFlow

Job description

Job Description

The Role:

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, 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.

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].

GMwill provide aone-time lumpsum taxable stipend payment to eligible students selected for the2027Student 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.

About GM

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