About the role#
This summer internship focuses on conducting original research and developing practical AI and machine learning methods to support engineering and product development. You will work within the GM R&D team to bridge the gap between theoretical research and real-world application. The role is designed for a current PhD student who brings depth in advanced AI and the ability to collaborate across disciplines. You will work alongside senior researchers and engineering partners to build prototypes, validate results, and contribute to technical roadmaps.
What you'll do#
- Conduct original research in areas such as causal AI, neurosymbolic AI, world models, multimodal or generative AI, and engineering agents.
- Translate complex engineering and scientific challenges into well-defined research questions, hypotheses, and experimental plans.
- Design, implement, and evaluate machine learning models and proof-of-concepts using real, simulated, or experimental data.
- Develop methods that reason about physical systems, causal relationships, uncertainty, and complex interactions.
- Establish meaningful baselines and evaluation protocols, focusing on robustness, generalization, and interpretability.
- Build reproducible research code, data-processing workflows, and experiment pipelines.
- Communicate technical findings through research reports, presentations, publications, invention disclosures, and demonstrations.
- Participate in technical discussions to identify promising approaches and high-value experiments.
What you'll need#
- Current pursuit of a PhD in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, Statistics, Computational Science, Engineering, Physics, or a related field.
- Ability to work full-time (40 hours per week) during the summer of 2027.
- Demonstrated research depth supported by a dissertation, publications, patents, open-source work, or comparable technical contributions.
- Strong foundations in machine learning and deep learning, with working knowledge of optimization, probability, statistics, numerical methods, or scientific computing.
- Experience developing and evaluating models in Python using frameworks such as PyTorch, JAX, or TensorFlow.
- Ability to formulate experiments, select metrics, analyze results, and communicate technical conclusions.
- Strong verbal and written communication skills.
- Preference for experience in causal inference, counterfactual reasoning, neurosymbolic methods, multimodal AI, large language models, or agentic systems.
- Preference for research or internship experience in automotive, mobility, aerospace, industrial, energy, defense, or national laboratory settings.
Location & details#
- Location: Mountain View, California.
- Work Modality: Hybrid (requires reporting to the site at least three times per week).
- Employment: Full-time internship for Summer 2027.
- Eligibility: This role is subject to export control restrictions and requires the candidate to be a U.S. Person (U.S. citizen, permanent resident, asylee, or refugee). General Motors does not provide immigration-related sponsorship for this position.
About General Motors
General Motors is a public company based in Detroit, Michigan. It operates in the motor vehicle manufacturing industry. The company employs over 106,000 people. Its work spans the design and production of cars, trucks, and transportation technology, including software, electrification, and AI.
How to get in at General Motors
Securing an internship at General Motors requires a proactive approach to the application timeline. Because early applicants are often reviewed before the volume of candidates becomes unmanageable, Intern Insider sends an instant alert the moment a role matching your target is published. This allows you to submit your materials among the first group of applicants. Reaching out to the right person can also change your outcome. Intern Insider surfaces the recruiters behind the company's roles, which lets you contact them directly to ask about the position or a potential referral. This direct connection often improves response rates compared to submitting an application into a general queue.
