Summer AI/ML Research Intern — Causal & Generative Models

General Motors LLC; United States; Hybrid; USD 115,000 - 152,000; Full time

Job summary

General Motors is seeking an early-career AI/ML research intern to conduct original research and develop practical methods across causal AI, neurosymbolic AI, world models, multimodal/generative AI, or engineering agents.

The role targets a current PhD student, requires full-time summer work (40 hours per week), and a hybrid arrangement in Mountain View, CA, with collaboration across GM R&D and engineering teams to move promising ideas toward impact.

Qualifications

• 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.
• Strong verbal and written communication skills and the ability to collaborate effectively with researchers, engineers, and technical stakeholders.
• Ability to work on-site or in a hybrid research environment in accordance with GM work practices and project needs.

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.

Skills

Python programming

Communication skills

Collaboration

Education

Pursuing PhD in CS/AI/ML/Math

Tools

PyTorch

JAX

TensorFlow

Job description

General Motors is seeking an early-career AI/ML research intern to conduct original research and develop practical methods across causal AI, neurosymbolic AI, world models, multimodal/generative AI, or engineering agents.

The role targets a current PhD student, requires full-time summer work (40 hours per week), and a hybrid arrangement in Mountain View, CA, with collaboration across GM R&D and engineering teams to move promising ideas toward impact.