General Motors
2027 Summer Intern - Research and Development - AI/Machine Learning
Mountain View, California, United States of America · Hybrid · Internship · Posted Oct 12, 2026
- Education
- Pursuit of a PhD in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, Statistics, Computational Science, Engineering, Physics, or a related field.
- Visa sponsorship
- GM does not provide immigration-related sponsorship now or in the future. The role requires a U.S. Person (U.S. citizen, U.S. permanent resident, asylee, or refugee).
About the job
Conduct original AI/ML research and develop, evaluate, and communicate research prototypes addressing engineering and scientific problems at GM.
What you'll do
- Develop and evaluate AI/ML methods and proof-of-concepts for engineering or scientific challenges.
- Design experiments, baselines, and evaluation protocols; analyze results, robustness, and limitations.
- Collaborate with researchers and engineering partners and communicate findings through reports, presentations, publications, and demonstrations.
What they ask for
- Currently pursuing a PhD in a listed or related field and must be a current student.
- Able to work full-time, 40 hours per week during the summer months.
- Demonstrated research depth in a relevant area and strong foundations in machine learning and deep learning.
- Experience developing and evaluating models in Python and frameworks such as PyTorch, JAX, TensorFlow, or comparable tools.
Nice to have
- Research experience in causal inference, counterfactual reasoning, neurosymbolic methods, or world models for engineering applications.
- Experience with multimodal AI, large language models, agentic systems, retrieval and reasoning, or AI-assisted engineering and software development.
Pay
$10,300–$13,600 monthly (base salary)
Tools
Python, PyTorch, JAX, TensorFlow
AI focus
Original research in advanced AI/ML, including causal, neurosymbolic, multimodal and generative AI, world models, and engineering agents; developing and evaluating methods and prototypes for real engineering and scientific applications.
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