May Mobility
Director, System Safety
$165K–$250K
USA - Remote +1 · Remote · Director · Full-time · Posted May 5, 2026
- Location
- USA - Remote
Ann Arbor, Michigan, United States - Experience
- 8+ years in product or system safety engineering, including 3+ years on L3+ AV or comparable hard-tech safety programs; 3+ years managing engineers, including managing a manager or leading senior individual contributors with readiness to manage managers.
- Education
- BA/BS degree in engineering, computer science, or a related technical field; advanced degree preferred.
About the job
Leads May Mobility’s product safety function and team, driving safety cases, gated driver-out deployment, Safety Management System development, and L4 commercial expansion in the US and Japan.
What you'll do
- Lead safety teams and own the L4 safety case framework, including analysis and runtime monitoring for the autonomy stack.
- Drive driver-out deployment gates with commercial partners and build out the Safety Management System and safety roadmap.
- Lead safety architecture, Japan compliance activities, partner and regulator engagement, and hiring and development of safety leaders.
What they ask for
- 8+ years in product or system safety engineering, including 3+ years on L3+ AV or comparable hard-tech safety programs.
- BA/BS in engineering, computer science, or a related technical field.
- Authored safety case work products on a shipping vehicle or equivalent cyber-physical program, and contributed to a driverless or driver-out commercial deployment milestone.
- 3+ years managing engineers, plus experience working with regulators.
Nice to have
- Advanced degree; working knowledge of ISO 21448 SOTIF, ISO 8800, and ISO 26262.
- AV or AI-driving company experience with an end-to-end learned or ML-heavy autonomy stack; published learned-component safety framework or runtime-monitoring methodology.
Pay
$165,000—$250,000 USD (base salary)
Tools
MPDM, ISO 21448, ISO 8800, ISO 26262, ISO/SAE 21434, C++, Python, Jupyter
AI focus
Develops ML-aware safety arguments for learned autonomy components, including runtime monitoring, distribution-shift mitigation, uncertainty quantification, and data-driven safety analysis.
Model-written summary · the original posting is authoritative