Safety Engineer Jobs

About The Position

Requirements

• 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.
• 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.
• Ability to lead cross-functional technical initiatives and communicate complex findings clearly.

Nice To Haves

• 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 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.

Responsibilities

• Define, standardize, and maintain core AV safety performance indicators, including collisions, near-misses, safety-critical interventions, traffic compliance, and surrogate safety metrics.
• Establish metric definitions, exposure denominators such as VMT, data-quality requirements, automated calculation pipelines, and change-management governance for safety metrics.
• Develop leading and lagging indicators to monitor safety across software releases, operational design domains, and structured scenario taxonomies.
• Develop quantitative benchmarking frameworks comparing AV safety performance with human-driving baselines, counterfactual models, public datasets, and regulatory disclosures.
• Evaluate comparability across operators and human drivers, accounting for exposure normalization, operating conditions, event severity, and reporting differences.
• Establish scenario-based and exposure-normalized targets aligned with partner and external readiness expectations.
• 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.
• Analyze rare and high-severity events, including 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 change-management procedures and governance for approval and iteration of safety metrics and benchmarking targets.
• Communicate technical evidence, uncertainty, and safety-readiness assessments to internal executives, partner stakeholders, and regulatory bodies.

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• 401(k) with company match
• Health savings accounts
• Life insurance
• Pet insurance