Staff Safety Engineer, Safety Metrics and Benchmarking

Motional

. Lead the definition, development, and operationalization of safety metrics and benchmarking methodologies for Level 4 autonomous vehicle and robotaxi deployment .

Posted 11/5/2026full-timeRemote • Nevada • United StatesLead💰 $144,000 - $191,250 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and operationalizing safety metrics for Level 4 autonomous vehicles, utilizing statistical modeling and risk assessment methodologies. Proven ability to lead cross-functional teams and communicate complex safety findings to stakeholders.

Highest-signal resume keywords

Statistical InferenceRisk QuantificationPython ProficiencySafety Engineering ExperienceCross-Functional Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical ModelingCollision Severity ModelingRisk AssessmentQuantitative Safety MetricsSurrogate Safety MeasuresCounterfactual ModelingNear-Miss TrackingData AnalysisSimulation-Based ValidationRelease-Readiness Evaluation

Soft Skills

Clear CommunicationTechnical Leadership

Tools & Technologies

SQLAutomated Calculation Pipelines

Industry Keywords

Level 4 Autonomous DrivingRobotaxi OperationsSafety Case FrameworksUL 4600ISO 21448ISO 8800SAE J3237ISO 26262BenchmarkingHuman Benchmark Models

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Lead the definition, development, and operationalization of safety metrics and benchmarking methodologies for Level 4 autonomous vehicle and robotaxi deployment
Establish quantitative frameworks to measure AV safety performance across on-road testing, simulation, and external benchmarks
Develop methodologies for risk estimation, counterfactual modeling, near-miss tracking, and collision severity assessment
Define and maintain AV safety performance indicators, including collisions, near-misses, safety-critical interventions, traffic compliance, and surrogate safety metrics
Establish metric definitions, exposure denominators, data quality requirements, automated calculation pipelines, and change-management governance
Develop leading and lagging indicators across software releases, operational design domains, and scenario taxonomies
Build benchmarking frameworks comparing AV safety with human-driving baselines, counterfactual models, public datasets, and regulatory disclosures
Apply statistical inference, uncertainty quantification, Bayesian methods, and risk modeling
Analyze rare and high-severity events and establish confidence intervals, sample-size requirements, and statistical evidence thresholds
Build collision severity and injury-risk models
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
Communicate technical evidence, uncertainty, and safety readiness assessments to executives, partners, and regulatory bodies

Requirements

What you’ll need

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 (preferred)
Experience with Level 4 autonomous driving, robotaxi operations, or other safety-critical systems (preferred)
Experience benchmarking AV safety using public reports, regulatory disclosures, industry datasets, or published research (preferred)
Familiarity with scenario taxonomies, safety case frameworks, and standards such as UL 4600, ISO 21448 (SOTIF), ISO 8800, SAE J3237, and ISO 26262 (preferred)
Experience with collision severity modeling, injury-risk prediction, or human benchmark datasets (preferred)

Benefits

Comp & perks

Medical insurance
Dental insurance
Vision insurance
401(k) with a company match
Health savings accounts
Life insurance
Pet insurance
Potential bonus compensation
Potential company equity
Equal employment opportunity and inclusive work environment