Director, System Safety

Job Summary

The Director, System Safety leads May Mobility's day-to-day product safety function — driving the L4 safety case, the Driver-Out (DO) gated deployment plan with strategic ride-hail partners, and the Safety Management System (SMS) build-out. Reporting to the VP, Safety & Validation, the role manages a multi-disciplinary team across systems and programs, test execution, and safety risk and process. The Director partners with the VP on external regulatory and partner relationships and is the primary internal owner of the safety roadmap. This role inherits a mature roadmap and bench, and will execute the next 18 months of L4 commercial expansion across the US and Japan.

Essential Responsibilities

• Lead a multi-disciplinary team across systems, test execution, and safety risk and process/SMS
• Drive the Driver-Out (DO) gated deployment plan with strategic ride-hail partners (Lyft, Uber) — internal gating, partner safety onsites, and recommendation packages for go/no-go decisions
• Own the Safety Case Framework for May's L4 systems, including HARA, FTA, FMEA, and runtime-monitor coverage for the MPDM-based decision-making stack, with ML-aware safety analysis
• Build out and operationalize the Safety Management System (SMS): incident risk assessment process, risk register automation, recall response plan, safety gating test suite, and the Product Safety Competency Register.
• Define safety architecture — partner with Autonomy, Validation, and Hardware to ensure safety-by-construction across future releases (scalable runtime monitoring, ODD enforcement, Driver-Anomaly response systems).
• Execute on May's L4 Japan compliance plan — partner with the Systems & Japan Programs Manager and the Japan Regulatory Safety Engineer on partner engagement and bilingual regulator response cadences.
• Co-lead strategic partner safety onsites (e.g., Uber Safety Onsite cadence) with the VP, Safety & Validation; own the technical narrative in OEM, ride-hail, and regulator forums.
• Hire and onboard the open safety roles and develop existing leaders in the org.

Skills and Abilities

• L4 Safety Case Architecture: Owns the safety case end-to-end — including ML-aware analysis (ISO 21448, ISO 8800). Has personally contributed to an L4 safety case, not only reviewed one.
• AI-Native Safety Mindset: Has built safety arguments for learned components (neural-network planners, transformer-based driving stacks, end-to-end models) where the V-model alone is insufficient. Comfortable defining runtime monitors, distributional-shift mitigations, and uncertainty-quantification strategies that anchor a credible safety case for ML-based decision-making.
• Data-Driven Safety: Has used vehicle telemetry, simulation results, or operational metrics to change a safety roadmap, gate a release, or escalate a risk. Partners naturally with data science and ML engineers to define safety KPIs and guard-rails grounded in evidence.
• Driver-Out / Driverless Deployment Execution: Has been a key contributor to the gating, partner-readiness, and regulator steps required to remove the safety driver from a commercial AV deployment. Comfortable owning the technical recommendation behind a go/no-go decision.
• Partner & Regulator Engagement: Co-leads safety conversations with strategic commercial partners (ride-hail, OEM, transit agency) and regulators alongside an executive sponsor — represents technical depth credibly without losing the audience.
• People Leadership: Has managed managers or has managed a team of senior ICs and is ready to step up to manager-of-managers. Track record of hiring under ambiguity and onboarding leaders into existing programs.
• SMS and Safety Process Build-Out: Has contributed to standing up or substantively expanding a Safety Management System — incident investigation, near-miss reporting, risk register operationalization, recall process. Cross-pollination from aviation SMS, rail, or another safety-critical industry is welcome.
• Operational Bias to Closure: Pulls plans through gates. Comfortable in a startup pace where the safety org is being built in parallel with commercial deployment, including across multiple time zones.
• Communication: Demonstrated ability to communicate, present and influence credibly and efficiently at all levels of the organization, including executive and C-level.

Qualifications and Experience

• 8+ years in product or system safety engineering, with 3+ years on L3+ AV or comparable hard-tech safety programs (aviation, rail, robotics).
• Bachelor's or Master's degree in engineering, computer science, or a related technical field; advanced degree preferred.
• Direct experience authoring safety case work products (HARA, FTA, FMEA, safety case construction) on a shipping vehicle or equivalent cyber-physical program.
• Hands-on contribution to at least one driverless or driver-out commercial deployment milestone — named on the safety recommendation.
• 3+ years managing engineers, including at least one stint managing a manager OR leading a team of senior ICs and ready to step up to manager-of-managers.
• Experience working with regulators (NHTSA, FMCSA, FTA, state DMVs, MLIT, or international equivalents) — direct or in close partnership with an executive sponsor.
• Working knowledge of ML-driven decision-making safety standards (ISO 21448 SOTIF, ISO 8800) and traditional functional safety (ISO 26262) preferred.
• Experience in Automotive, AV, Aerospace, or another safety-critical regulated industry is highly preferred.
• Proven success working in a start-up or small business where there are minimal resources, nothing comes easy, and things don't always work as planned.
• Prior experience at an AV or AI-driving company with an end-to-end learned or ML-heavy autonomy stack.
• Experience contributing to a published safety assurance framework or runtime-monitoring methodology for learned components.
• Familiarity with the safety-cybersecurity intersection (ISO/SAE 21434, NHTSA Cybersecurity Best Practices) is a plus — May's product cyber and safety functions partner closely.
• Ability to undergo a driving record check.
• Strongly preferred: prior experience at an AV or AI-driving company with an end-to-end learned or ML-heavy autonomy stack.
• Preferred: experience contributing to a published safety assurance framework or runtime-monitoring methodology for learned components.
• Preferred: experience working with regulators (NHTSA, FMCSA, FTA, state DMVs, MLIT, or international equivalents) — direct or in close partnership with an executive sponsor.