General Motors
2027 Summer Intern - Research and Development - Aftertreatment Catalysis
Warren, Michigan, United States of America · Hybrid · Internship · Posted Oct 30, 2026
- Education
- Currently pursuing a master’s or doctoral degree in chemistry, chemical engineering, materials science, materials engineering, mechanical engineering, computer science, data science, physics, or a related STEM field.
- Visa sponsorship
- GM does not provide immigration-related sponsorship now or in the future, including sponsorship or immigration support for work authorization. The role requires the successful candidate to be a U.S. Person (U.S. citizen, U.S. permanent resident, asylee, or refugee).
About the job
Conduct research on automotive aftertreatment catalysts, contributing to experiments, modeling, data analysis, or AI/ML-enabled materials discovery for emissions-control technologies.
What you'll do
- Design and analyze catalyst experiments, characterize materials, and investigate catalyst performance and durability.
- Develop models and use data science or AI/ML methods to analyze catalyst datasets and accelerate screening or materials discovery.
- Document and present findings, collaborate with research and engineering partners, and follow laboratory safety and data-quality practices.
What they ask for
- Currently pursuing a master’s or doctoral degree in a relevant STEM field.
- Available to work full-time, 40 hours per week during the summer internship.
- Demonstrated interest in at least one listed area, such as catalysis, laboratory experimentation, modeling, data science, or machine learning.
- Must be a U.S. Person; GM does not provide immigration-related sponsorship for this role.
Tools
Python, MATLAB, Fourier-transform infrared spectroscopy, diffuse-reflectance infrared Fourier-transform spectroscopy
AI focus
May apply statistical learning, machine learning, optimization, or uncertainty quantification to catalyst and materials datasets, and explore AI/ML for materials discovery, composition optimization, performance prediction, and experiment planning.
Model-written summary · the original posting is authoritative