2027 Summer Intern - Research and Development - AI/Machine Learning

General Motors; Mountain View (CA); On-site; USD 55,000 - 83,000; Full time

Job summary

General Motors R&D in Mountain View seeks a highly motivated PhD candidate for an AI/ML research internship. You will conduct original research, build proofs-of-concept, and evaluate methods on real engineering data with senior researchers and engineers to move ideas toward impact.

You should have deep knowledge in at least one AI area, be proficient in Python and ML frameworks, and be able to communicate results clearly.

Qualifications

• Pursuit of a PhD in Computer Science, AI, ML, or related field.
• Must be a current student.
• Able to work full-time, 40 hours per week during summer months.
• Demonstrated depth in at least one relevant area with scholarly contributions.
• Strong foundations in ML and deep learning with exposure to optimization/statistics.

Responsibilities

• Conduct original research in 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

ML foundations

Python proficiency

Experiment design

Communication skills

Independent researcher

Reproducibility practices

Education

PhD candidate (CS/AI/ML)

Current student

Tools

PyTorch

JAX

TensorFlow

Job description

Job Description

The Role:

Conduct original research and develop practical AI/ML methods that accelerate GM's research, engineering, and product development capabilities. This role is intended for a recent or soon-to-be-completed Ph.D. who brings strong depth in one or more advanced AI areas and the ability to work across disciplines. The intern will develop proof-of-concepts (POCs), 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 Team:

GM R&D is seeking an outstanding early-career intern to contribute to advanced work in Artificial Intelligence, Machine Learning, and Large Language Models. The role is designed for a fresh Ph.D. It emphasizes research depth, originality, experimental rigor, and growth toward independent technical leadership. The successful candidate will work with senior researchers, technical fellows, and engineering collaborators to formulate important problems, develop new methods, build research prototypes, and validate results. The work may span causal reasoning, neurosymbolic AI, world models, multimodal and generative AI, engineering agents, or related areas. The intern will be expected to connect theory and experimentation with real engineering needs while maintaining high standards for reproducibility, explainability, uncertainty awareness, and responsible AI.

What You'll Do:

• 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.

More About Who You Are

• A curious and intellectually rigorous researcher who enjoys working on difficult, open-ended problems.
• Deeply grounded in at least one technical area, while eager to learn and collaborate across adjacent AI, computational science, and engineering disciplines.
• Motivated by research that can create measurable value in real-world engineering workflows, not only benchmark performance.
• Comfortable moving between mathematical formulation, algorithm development, software implementation, and empirical validation.
• Able to explain complex ideas, assumptions, limitations, and results to both technical and general audiences.
• Independent in driving research tasks to completion while actively seeking feedback and working effectively with senior mentors and cross-functional teams.
• Committed to careful experimentation, reproducibility, safety, explainability, and responsible use of AI.

Required 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