General Motors
2027 Summer Intern – AI & Hardware Analytics, ADPT
Warren, Michigan, United States of America +1 · On-site · Internship · Posted Oct 27, 2026
- Location
- Warren, Michigan, United States of America
Milford, Michigan, United States of America - Education
- Working toward a Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Industrial Engineering, Manufacturing Engineering, Electrical Engineering, Mechanical Engineering, or a related field.
- Visa sponsorship
- GM does not provide immigration-related sponsorship for this role; applicants who need sponsorship now or in the future should not apply.
About the job
Supports AI and hardware process efficiency initiatives by analyzing production data, developing analytical tools, and recommending process improvements for automated driving systems.
What you'll do
- Analyze production, quality, equipment, test, and operational data; apply statistical and AI techniques to identify opportunities and support predictive insights.
- Develop data pipelines, analytical models, automated workflows, dashboards, and reports for engineering and production teams.
- Evaluate process performance, support root-cause investigations, measure process changes, and communicate findings and recommendations.
What they ask for
- Working toward a Bachelor's or Master's degree in a listed or related field.
- Coursework or project experience in Python, SQL, MATLAB, statistics, data visualization, or machine learning.
- Able to work full-time, 40 hours per week.
- Must be graduating between December 2027 and June 2028 and intend to return to a degree program after the internship.
Nice to have
- Experience with Python libraries or tools such as pandas, NumPy, scikit-learn, PyTorch, or TensorFlow.
- Familiarity with listed analytics tools or methods, or basic understanding of manufacturing, hardware development, robotics, automated testing, embedded systems, or production operations.
Pay
$8,100 – $8,700 per month (base salary)
Tools
Python, SQL, MATLAB, pandas, NumPy, scikit-learn, PyTorch, TensorFlow
AI focus
Applies statistical analysis, machine learning, anomaly detection, and other AI techniques to production and operational data for predictive insights and process optimization.
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