layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - 2027 Summer Intern - Research and Development - AI/Machine Learning at General Motors

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 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.

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

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