Entry-Level – AI/Machine Learning Researcher

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About the role

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 researcher 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 researcher 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 researcher 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 wor

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