Summer Intern - Research and Development - AI/Machine Learning

About This Role

This internship is for a recent or soon-to-be-completed Ph.D. with strong depth in advanced AI, offering the opportunity to conduct original research and develop practical AI/ML methods. The role accelerates GM’s research, engineering, and product development capabilities, focusing on experimental rigor and growth toward independent technical leadership.

Responsibilities

• Conduct original research in one or more advanced AI areas such as causal AI, neurosymbolic AI, world models, multimodal or generative AI or engineering agents
• Translate engineering and scientific challenges into well-defined research questions, hypotheses, algorithms, and experimental plans
• Design, implement, and evaluate machine learning models and proof-of-concepts using real, simulated, experimental, or engineering data
• Develop methods that can reason about physical systems, causal relationships, uncertainty, constraints, temporal evolution, or complex interactions among components
• Establish meaningful baselines and evaluation protocols; analyze failure modes, robustness, generalization, interpretability, and uncertainty rather than relying only on headline accuracy
• Build reproducible research code, data-processing workflows, and experiment pipelines using modern deep learning practices
• Collaborate with GM R&D researchers and engineering partners to understand domain constraints, validate research results, and identify pathways from POCs to practical use
• Communicate technical findings through research reports, presentations, publications, invention disclosures, demonstrations, and clear documentation
• Contribute to technical discussions and research roadmaps by identifying promising approaches, important risks, and high-value experiments
• Participate in a collaborative research culture that values intellectual curiosity, constructive peer review, responsible innovation, and continuous learning

Requirements

• Pursuit of a PhD in one of the following areas: Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, Statistics, Computational Science, Engineering, Physics, or a related field
• Must be a current student
• Able to work fulltime, 40 hours per week during the summer months
• Demonstrated research depth in at least one relevant area, supported by a dissertation, publications, patents, open-source work, research software, or comparable technical contributions
• Strong foundations in machine learning and deep learning, with working knowledge of optimization, probability/statistics, numerical methods, or scientific computing as appropriate to the research area
• Experience developing and evaluating models in Python and frameworks such as PyTorch, JAX, TensorFlow, or comparable tools
• Ability to formulate experiments, select appropriate metrics and baselines, analyze results, and communicate technical conclusions with appropriate caveats