Hybrid Summer Intern: AI-Driven Vehicle Diagnostics

General Motors Company; Warren (MI); Hybrid; USD 28,000 - 41,000; Full time

Job summary

General Motors Company in Warren, Michigan is seeking a 2027 Summer Intern for Research and Development in Vehicle Health Management (VHM). You will explore physics-based modeling, data science, and AI to build scalable diagnostics and prognostics solutions for vehicle systems.

The role combines hands-on experiments with ML workflows, offering collaboration with cross-functional teams and exposure to cost-reduction and engineering efficiency initiatives.

Qualifications

• Pursuit of an advanced degree (Master or PhD) in Electrical Engineering, Mechanical Engineering, Aerospace Engineering, Computer Science, AI, or related field.
• Must be a current student for the internship.
• Ability to work full-time, 40 hours per week during summer.
• Background in fault detection, diagnostics, and prognostics.
• Understanding of AI/ML workflows including data prep, model training and deployment.

Responsibilities

• Generate innovative ideas in VHM related areas using physics-based and data-driven methods.
• Develop next-generation technologies for diagnosis, prognosis, and fault-tolerant controls.
• Execute research plans: concept gen, prototype, system implementation, performance evaluation.
• Document and present results.

Skills

Fault detection and isolation

Diagnostics and prognostics

Root cause analysis

AI/ML development workflows

Data preparation

Education

Master or PhD in Electrical Engineering or related field

Tools

Python

PyTorch

TensorFlow

PySpark

MATLAB/Simulink

Job description

General Motors Company in Warren, Michigan is seeking a 2027 Summer Intern for Research and Development in Vehicle Health Management (VHM). You will explore physics-based modeling, data science, and AI to build scalable diagnostics and prognostics solutions for vehicle systems.

The role combines hands-on experiments with ML workflows, offering collaboration with cross-functional teams and exposure to cost-reduction and engineering efficiency initiatives.