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

General Motors
USA
Full-time
Intern
$10,300–$13,600 / month
Discovered 1 weeks ago
jaxpythonpytorchtensorflow

Full Job Posting

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.

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.
  • GM does not provide immigration-related sponsorship for this role. Do not apply for this role if you will need GM immigration sponsorship now or in the future. This includes direct company sponsorship, entry of GM as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc).
  • The position is subject to export control restrictions and requires the successful candidate to be a U.S. Person (U.S. citizen, U.S. permanent resident, asylee or refugee).
  • Work Arrangement:
  • Hybrid: This role is categorized as hybrid. This means the successful candidate is expected to report to Moutain View, CA three times per week, at minimum [or other frequency dictated by the business].
  • Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position, as well as geography of the selected candidate.
  • The salary range for this role is $10,300–$13,600 monthly.
  • The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
  • GM will provide a one-time lump sum taxable stipend payment to eligible students selected for the 2027 Student Program - to help facilitate administration of relocation benefits, please apply using the permanent address you would move from.
  • Start dates for this internship role are May 24th & June 14th of 2027.

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 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.

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