Staff Safety Engineer, Safety Metrics and Benchmarking

We are seeking an experienced Staff Safety Engineer to lead the definition, development, and operationalization of safety metrics and benchmarking methodologies for Level 4 autonomous vehicle (AV) and robotaxi deployment.

In this role, you will establish quantitative frameworks to measure AV safety performance across on-road testing, simulation, and external benchmarks. You will develop statistically rigorous methodologies for risk estimation, counterfactual modeling, near-miss tracking, and collision severity assessment to guide deployment and software release decisions.

Partnering across Safety Engineering, Systems Readiness & Performance, Autonomy, Data Science, and Fleet Operations, you will translate complex data into actionable metrics, governance frameworks, and executive evidence to ensure safe driverless commercial launch.

• Define, standardize, and maintain core AV safety performance indicators (SPIs), including vehicle collisions, near-misses, safety-critical interventions, traffic compliance, and surrogate safety metrics
• Establish standardized metric definitions, exposure denominators (e.g., VMT), data quality requirements, automated calculation pipelines, and change-management governance for safety metrics.
• Develop leading and lagging indicators to monitor safety performance across software releases, operational design domains (ODDs), and structured scenario taxonomies.
• Develop quantitative benchmarking frameworks comparing AV safety performance against human-driving baselines, counterfactual models, public datasets (e.g., SHRP2, NASS-GES), and regulatory disclosures.
• Evaluate comparability across operators and human drivers, accounting for exposure normalization, operating conditions, event severity levels, and reporting differences.
• Establish scenario-based and exposure-normalized targets that provide meaningful safety criteria and align with partner/external readiness expectations.
• Clearly communicate benchmarking assumptions, limitations, and statistical uncertainty.
• Apply statistical inference, uncertainty quantification, Bayesian methods, and risk modeling to evaluate AV behavior and scenario-level performance.
• Develop methods to analyze rare and high-severity events, establishing confidence intervals, sample-size requirements, and statistical evidence thresholds for release decisions.
• Build collision severity and injury-risk models to evaluate impact dynamics, delta-V, and potential harm mitigation.
• Connect quantitative findings to engineering investigations, safety mitigations, and release readiness assessments.
  
  

• Provide technical leadership and cross-functional alignment across Safety Case, Systems Engineering, Simulation, Data Science, and Policy teams.
• Lead the establishment of change management procedures and governance for approval and iteration of safety metrics and benchmarking targets.
• Communicate technical evidence, uncertainty, and safety readiness assessments effectively to internal executives, key partner stakeholders, and regulatory bodies.
  
  

• Bachelor's degree in Engineering, Computer Science, Statistics, Mathematics, Data Science, or a related technical field.
• 8+ years of experience in safety engineering, autonomous driving evaluation, statistical modeling, simulation-based validation, or AV risk assessment.
• Proven track record of defining quantitative safety metrics, SPIs, surrogate safety measures, and human benchmark models.
• Proficiency in Python and SQL, with experience analyzing large-scale operational or safety-related datasets.
• Strong expertise in statistical inference, rare-event analysis, counterfactual modeling, risk quantification, and release-readiness evaluation.
• Proven ability to lead cross-functional technical initiatives and communicate complex findings clearly.
  
  

• Master's or Ph.D. in a relevant technical or quantitative discipline.
• Experience with Level 4 autonomous driving, robotaxi operations, or other safety-critical systems.
• Experience benchmarking AV safety using public reports, regulatory disclosures, industry datasets, or published research.
• Familiarity with scenario taxonomies, safety case frameworks, and applicable industry standards such as UL 4600, ISO 21448 (SOTIF), ISO 8800, SAE J3237, and ISO 26262.
• Experience with collision severity modeling, injury-risk prediction, or human benchmark datasets

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We aren't just developing driverless cars; we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect. Our team is made up of engineers, researchers, innovators, dreamers and doers, who are creating a technology with the potential to transform the way we move.

We’re creating first-of-its-kind technology that will transform transportation. To do so successfully, we must design for everyone in our cities and on our roads. We believe in building a great place to work through a progressive, global culture that is diverse, inclusive, and ensures people feel valued at every level of the organization. Diversity helps us to see the world differently; it’s not only good for our business, it’s the right thing to do.  

Our team is behind some of the industry's largest leaps forward, including the first fully-autonomous cross-country drive in the U.S, the launch of the world's first robotaxi pilot, and operation of the world's longest-standing public robotaxi fleet. We’re driven to scale; we’re moving towards commercialization of our technology, and we need team members who are ready to embrace change and challenges.

Formed as a joint venture between Hyundai Motor Group and Aptiv, Motional is fundamentally changing how people move through their lives. Headquartered in Boston, Motional has operations in the U.S and Asia. For more information, visit  www.Motional.com and follow us on Twitter, LinkedIn, Instagram and YouTube.

Motional AD Inc. is an EOE. We celebrate diversity and are committed to creating an inclusive environment for all employees. To comply with Federal Law, we participate in E-Verify. All newly-hired employees are queried through this electronic system established by the DHS and the SSA to verify their identity and employment eligibility.