General Motors
Entry Level - Researcher - System Safety
Warren, Michigan, United States of America · Hybrid · Entry level · Full-time · Posted Oct 12, 2026
- Experience
- Relevant experience is required; no number of years specified.
- Education
- Advanced degree (MS or PhD) in Engineering, Mathematics, Computer Science, Robotics, Safety Management, or a related technical field.
- Visa sponsorship
- Must be a U.S. citizen or permanent resident (Green Card Holder), asylee, or refugee; GM does not provide immigration-related sponsorship, including for work authorization requiring company support.
About the job
Research and advance system safety analyses and safety cases for complex, safety-critical technologies, assessing evidence and supporting safe development and regulatory alignment.
What you'll do
- Develop, assess, and continuously improve safety analyses and system safety cases; define claims, evidence strategies, and sufficiency criteria.
- Evaluate new technologies, integrate cross-disciplinary evidence, identify gaps and risks, and communicate recommendations through safety-case materials.
- Improve safety evidence traceability and processes, assess impacts of system changes, and research emerging safety-assurance methods.
What they ask for
- Advanced degree (MS or PhD) in Engineering, Mathematics, Computer Science, Robotics, Safety Management, or a related technical field.
- Must be graduating between August 2026 - June 2027; able to work fulltime, 40 hours per week.
- 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 (FuSa), ISO 21448 (SOTIF), ISO/PAS 8800 (AI safety), 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, probabilistic safety arguments, evidence sufficiency, or Goal Structuring Notation (GSN).
Tools
AI/ML, 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 AI safety; integrate AI/ML evidence into safety cases.
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