Risk Management Lead

Job Summary

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.

Essential Responsibilities

Risk Framework and Governance

• 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.

Safety and Technical Risk

• 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.

AI and Data Risk

• 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.

Regulatory and Deployment Risk

• 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.

Operational and Fleet Risk

• 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
• Feedback loops from field data to engineering risk registers

Decision Support and Leadership Communication

• Maintain a top-risk portfolio with clear ownership and mitigation status.
• Prepare executive-level risk reviews and go/no-go decision materials.
• Clearly communicate residual risk and tradeoffs to leadership.

Culture and Enablement

• Promote a strong risk-aware and safety-first culture across the organization.
• Train and mentor teams on risk identification, documentation, and escalation.
• Act as an independent and trusted voice in technical and product discussions.

Qualifications and Experience

Candidates most successful in this role typically hold the following qualifications or comparable knowledge or experience:

Required Qualifications:

• Bachelor’s degree in Engineering or related technical field.
• 10+ years of experience in autonomous systems, automotive safety, systems engineering, or risk management.
• Hands-on experience with autonomous vehicle or ADAS systems.
• Strong understanding of AV architectures and system safety concepts.
• Ability to influence cross-functional teams without direct authority.

Desirable Qualifications:

• Experience developing or supporting autonomous vehicle safety cases.
• Familiarity with driverless or limited ODD deployments.

Physical Requirements

• Standard office working conditions which includes but not limited to:
• Prolonged sitting
• Prolonged standing
• Prolonged computer use

Want to learn more about our culture & benefits? Check out our website!

May Mobility is an equal opportunity employer.  All applicants for employment will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity or expression, veteran status, genetics or any other legally protected basis.   Below, you have the opportunity to share your preferred gender pronouns, gender, ethnicity, and veteran status with May Mobility to help us identify areas of improvement in our hiring and recruitment processes. Completion of these questions is entirely voluntary.  Any information you choose to provide will be kept confidential, and will not impact the hiring decision in any way. If you believe that you will need any type of accommodation, please let us know.

Note to Recruitment Agencies: May Mobility does not accept unsolicited agency resumes. Furthermore, May Mobility does not pay placement fees for candidates submitted by any agency other than its approved partners.