Entry-Level - AI and Machine Learning for Automation Researcher
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Role details
Tech stack
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.
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., * 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), 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].
To help facilitate administration of relocation benefits if you are selected, please apply using the permanent address you would move from.
About GM
Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.
Requirements
- Pursuing a masters or PhD. in Machine Learning, Physical Robotics, Computer Science, or a closely related field from a leading university., * 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).
About the company
We believe we all must make a choice every day - individually and collectively - to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team., General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.
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