2027 Summer Intern - Research and Development - AI/Machine Learning
Salary not listed • Mountain View, California, United States of America • Internship
Posted 19d ago
No salary is listed. Many US states require employers to post a pay range, so ask about pay early.
Job Location
Mountain View, California, United States of America
Tech Stack
Remote Work Policy
On-site
Employment Type
Internship
Categories
AI Research Engineer
About the job
GM R&D is seeking an outstanding early-career intern to contribute to advanced work in Artificial Intelligence, Machine Learning, and Large Language Models. This role is designed for a Ph.D. student who brings strong depth in one or more advanced AI areas and the ability to work across disciplines. The intern will conduct original research, develop proof-of-concepts, evaluate them on meaningful engineering or scientific problems, communicate findings clearly, and collaborate with senior researchers and engineering partners to move promising ideas toward impact. The work may span causal reasoning, neurosymbolic AI, world models, multimodal and generative AI, engineering agents, or related areas, emphasizing research depth, originality, experimental rigor, and growth toward independent technical leadership.
Responsibilities
- Conduct original research in advanced AI areas like causal AI, neurosymbolic AI, world models, multimodal or generative AI, or engineering agents.
- Translate engineering and scientific challenges into research questions, algorithms, and experimental plans.
- Design, implement, and evaluate machine learning models and proof-of-concepts using various data types.
- Develop methods for reasoning about physical systems, causal relationships, uncertainty, constraints, and complex interactions.
- Establish meaningful baselines and evaluation protocols, analyzing failure modes, robustness, generalization, interpretability, and uncertainty.
- Build reproducible research code, data-processing workflows, and experiment pipelines using modern deep learning practices.
- Collaborate with researchers and engineers to understand domain constraints, validate results, and identify pathways for practical use.
- Communicate technical findings through reports, presentations, publications, and documentation.
- Contribute to technical discussions and research roadmaps by identifying promising approaches and risks.
- Participate in a collaborative research culture valuing curiosity, peer review, responsible innovation, and learning.
Requirements
- Currently pursuing a PhD in 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 full-time (40 hours/week) during the summer months.
- Demonstrated research depth in at least one relevant area (supported by dissertation, publications, patents, open-source work, etc.).
- Strong foundations in machine learning and deep learning.
- Working knowledge of optimization, probability/statistics, numerical methods, or scientific computing.
- Experience developing and evaluating models in Python using frameworks like PyTorch, JAX, or TensorFlow.
- Ability to formulate experiments, select metrics, analyze results, and communicate technical conclusions.
- Strong verbal and written communication skills.
- Ability to collaborate effectively with researchers, engineers, and stakeholders.
- Ability to work on-site or in a hybrid research environment.