Entry Level - Researcher - System Safety

General Motors•Warren, MI

4d•Hybrid

About The Position

GM R&D is seeking a System Safety Researcher to work on research, assessment, and methodological advancement in system safety and safety assurance. You will perform safety analyses, develop and improve safety cases, define claims and sufficiency criteria with domain experts, integrate evidence across disciplines, and ensure alignment with product evolution, regulatory expectations, and industry best practices. The role works across engineering, research, AI/ML, systems engineering, functional safety, cybersecurity, validation, product safety, legal, regulatory, and leadership teams to strengthen traceability, evidence quality, and safety decisions. Conduct research and work on the development, assessment, and continuous improvement of system safety analyses for complex products and safety-critical systems. As part of the system safety case development, evaluate safety arguments, evidence strategies, and sufficiency criteria to support safe technology development and align with regulatory expectations, industry best practices, and enterprise safety objectives. Join General Motors Research & Development as a System Safety Researcher. You will contribute research and thought leadership in system safety, safety assurance, and evidence-based decision-making across current and emerging technology areas.

Requirements

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
Green Card Holder or US Citizen
Relevant experience in systems engineering, system safety, safety assurance, program leadership, or other 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.
Ability to decompose safety goals into clear requirements and evidence needs, synthesize ambiguous technical data, and evaluate evidence sufficiency.
Strong written and verbal communication skills, including the ability to translate complex technical concepts for non-technical audiences.
Passion for system safety research, safety assurance, and the safe deployment of complex technologies.

Nice To Haves

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, and evidence-sufficiency evaluation.
Familiarity with Goal Structuring Notation (GSN) or similar structured argumentation frameworks.
Experience with requirements management, traceability, and evidence management in safety-critical development lifecycles.
Experience engaging with external assessors, regulators, standards bodies, research institutions, or industry consortia.

Responsibilities

Conduct the research, development, governance, and continuous improvement of safety analyses and system safety cases for complex products and safety-critical systems.
Support system safety evaluation of newly developed technologies within R&D by defining evaluation approaches, reviewing evidence, identifying gaps, and communicating recommendations.
Author and present safety-case materials for product submissions, audits, internal reviews, external assessments, and executive or regulatory audiences.
Integrate and assess evidence from systems engineering, AI/ML, functional safety, cybersecurity, validation, product safety, and other relevant disciplines.
Perform impact analyses when system scope, architecture, operating context, platform, or release changes affect safety claims, criteria, or evidence.
Improve compliance, traceability, and evidence-tracking processes, including opportunities to automate safety-evidence collection across the engineering lifecycle.
Surface safety gaps, evidence weaknesses, and emerging risks, and drive timely escalation and resolution through appropriate forums.
Align safety-case claims with applicable standards, guidance, and best practices, including UL 4600, ISO 26262, ISO 21448, AVSC guidance, and ISO/PAS 8800.
Research emerging approaches in AI/ML safety validation, probabilistic safety arguments, evidence sufficiency, and structured safety-case development; synthesize findings into recommendations.