General Motors
2027 Summer Intern - Research and Development - System Safety
Warren, Michigan, United States of America · Hybrid · Internship · Posted Oct 12, 2026
- Experience
- No specific number of years stated.
- Education
- Pursuing an advanced degree (MS or PhD) in Engineering, Mathematics, Computer Science, Robotics, Safety Management, or a related technical field.
- Visa sponsorship
- Requires U.S. citizenship to be eligible for a Secret U.S. Security Clearance. GM does not provide immigration-related sponsorship for this role.
About the job
Research and advance system safety analyses and safety cases for complex, safety-critical technologies, integrating evidence and communicating safety assessments and recommendations.
What you'll do
- Develop, assess, and improve system safety analyses and safety cases; define safety claims, evidence needs, and sufficiency criteria.
- Integrate and evaluate evidence across engineering disciplines, identify safety gaps and risks, and communicate recommendations to technical and non-technical audiences.
- Research safety-assurance methods and improve traceability, compliance, and evidence-tracking processes.
What they ask for
- Must be a current student pursuing an advanced degree (MS or PhD) in Engineering, Mathematics, Computer Science, Robotics, Safety Management, or a related technical field.
- Able to work fulltime, 40 hours per week during the summer months.
- Relevant experience in systems engineering, system safety, safety assurance, program leadership, or complex software-intensive safety-critical environments; demonstrated experience developing, supporting, or assessing safety cases, assurance cases, or equivalent structured safety arguments.
- Knowledge of functional safety and safety assurance concepts, including ISO 26262, ISO 21448, ISO/PAS 8800, and UL 4600.
Nice to have
- Experience in automotive, advanced mobility, product safety, regulatory, certification, or safety-assurance research and practice.
- Understanding of AI/ML safety validation, statistical or probabilistic safety arguments, evidence-sufficiency evaluation, or Goal Structuring Notation (GSN).
Tools
ISO 26262, ISO 21448, ISO/PAS 8800, UL 4600, AVSC guidance, Goal Structuring Notation (GSN)
AI focus
Research emerging approaches in AI/ML safety validation and integrate AI/ML evidence into safety cases.
Model-written summary · the original posting is authoritative