Risk Management Lead

May Mobility is transforming cities through autonomous technology to create a safer, greener, more accessible world. Based in Ann Arbor, Michigan, May develops and deploys autonomous vehicles (AVs) powered by our innovative Multi-Policy Decision Making (MPDM) technology that literally reimagines the way AVs think.

Our vehicles do more than just drive themselves – they provide value to communities, bridge public transit gaps and move people where they need to go safely, easily and with a lot more fun. We’re building the world’s best autonomy system to reimagine transit by minimizing congestion, expanding access and encouraging better land use in order to foster more green, vibrant and livable spaces.

Since our founding in 2017, we’ve given more than 300,000 autonomy‑enabled rides to real people around the globe. And we’re just getting started. We’re hiring people who share our passion for building the future, today, solving real‑world problems and seeing the impact of their work. Join us.

The Risk Management Lead for Autonomous Vehicles is accountable for defining, implementing, and directing the comprehensive risk management strategy for autonomous driving systems, spanning the entire lifecycle from conceptualization through deployment and ongoing operation.

This position mandates the proactive identification, rigorous assessment, and systematic management of safety, technical, regulatory, AI/ML, and operational risks across hardware, software, AI, and vehicle integration domains. The role necessitates close collaboration with Systems Engineering, Safety, AI/ML, Product, Legal, Compliance, and Operations teams to facilitate informed decision‑making and ensure secure deployment.

• Develop and maintain the autonomous vehicle risk management framework, ensuring alignment with system safety standards and the product lifecycle process.
• Establish a comprehensive risk taxonomy encompassing functional safety, Safety of the Intended Functionality (SOTIF), AI/ML uncertainty, cybersecurity, regulatory compliance, operational hazards, and reputational exposures.
• Define standardized risk scoring methodologies, acceptance criteria, formal escalation pathways, and designated decision authority levels.

• Direct the identification and assessment of risks pertaining to perception, prediction, planning, and vehicle control systems.
• Sensor performance characteristics, redundancy provisions, degradation modes, and failure mechanisms.
• AI/ML limitations, operational edge cases, and inherent uncertainty.
• Human‑machine interaction, potential misuse, and operational assumptions.
• Liaise with System Safety teams to ensure adherence to ISO 26262, ISO 21448 (SOTIF), and UL 4600 standards.
• Hazard Analysis and Risk Assessment (HARA) processes.
• Failure Mode and Effects Analysis (FMEA), Fault Tree Analysis (FTA), System‑Theoretic Process Analysis (STPA), and related systematic analyses.

• Evaluate risks stemming from deficiencies in training data coverage, inherent bias, model drift over time, and validation gaps.
• Ensure the traceability of identified AI risks to corresponding mitigations, such as constraints imposed by the Operational Design Domain (ODD).
• Implementation of runtime monitoring and diagnostic capabilities.
• Defined fallback strategies and maintenance of safe operational states.
• Support the generation of model explainability artifacts and risk evidence for both internal scrutiny and external regulatory review.

• Identify and manage risks associated with autonomous vehicle regulations, required permits, granted exemptions, and mandatory reporting obligations.
• Contribute to the development of the safety case and regulatory engagement by furnishing clear, well‑structured evidence of risk management practices.
• Assess risks associated with driverless testing and deployment decisions.

• Evaluate risks in testing, validation, fleet operations, and scaling.
• Partner with fleet and operations teams on incident analysis and lessons learned, change management and release readiness, and feedback loops from field data…