AI/Machine Learning Researcher

About the Role

General Motors R&D is seeking an outstanding early-career researcher to contribute to advanced work in Artificial Intelligence, Machine Learning, and Large Language Models. This role is designed for a fresh Ph.D. and emphasizes research depth, originality, experimental rigor, and growth toward independent technical leadership.

Key Responsibilities

• 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 (POCs) 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.

Requirements & Tech Stack

• Deeply grounded in at least one technical area, while eager to learn and collaborate across adjacent AI, computational science, and engineering disciplines.
• Comfortable moving between mathematical formulation, algorithm development, software implementation, and empirical validation.
• Independent in driving research tasks to completion while actively seeking feedback and working effectively with senior mentors and cross-functional teams.
• Python
• Frameworks such as PyTorch, JAX, TensorFlow, or comparable tools.

Qualifications

• Pursuit or Completion of a Ph.D. in one of the following areas: Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, Statistics, Computational Science, Engineering, Physics, or a related field.
• 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.
• Able to work on-site or in a hybrid research environment.

Preferred Qualifications:

• Research experience in causal inference, counterfactual reasoning, neurosymbolic methods, and world models for engineering applications.
• Experience with multimodal AI, large language models, agentic systems, retrieval and reasoning, or AI-assisted engineering and software development.
• Evidence of research impact through high-quality publications, patents, invention disclosures, open-source contributions, awards, or successful technology demonstrations.
• Research or internship experience in automotive, mobility, aerospace, industrial, energy, defense, national laboratory, or university-industry collaboration settings.

Work Arrangement: Hybrid

Compensation: $128,000 - $279,000 (Base Salary)

Bonus Potential: Incentive pay program based on company performance, job level, and individual performance.

Benefits: Health and wellbeing benefit programs, including medical, dental, vision, retirement savings plan, paid vacation and holidays, tuition assistance, and GM vehicle discounts.