AI Engineer – Simulation & Scenario Validation

Overview As an AI Engineer specializing in Simulation & Scenario Validation, you will play a key role in ensuring that our autonomous driving systems are evaluated against the right scenarios, with sufficient coverage and rigor to support safe autonomous driving deployment. Working closely with simulation, autonomous driving, and safety teams, you will assess scenario coverage, analyze system performance, and identify gaps in our validation strategies. This role focuses on system-level validation, simulation effectiveness, and evidence-based assessment of autonomous driving readiness, rather than scenario generation or simulation infrastructure development. Responsibilities Develop and assess simulation-based validation strategies to ensure comprehensive coverage of safety-critical driving behaviors and operational design domains (ODDs). Analyze scenario libraries and validation datasets to identify coverage gaps, redundant tests, and underrepresented or high-risk driving conditions. Evaluate autonomous driving performance in simulation, analyzing system behavior, robustness, failure modes, and safety-critical interactions across diverse scenarios. Define and improve scenario coverage metrics, including behavioral coverage, ODD coverage, scenario diversity, and risk-based validation criteria. Collaborate with simulation, perception, and autonomous driving teams to guide scenario selection, test prioritization, and validation requirements. Correlate simulation results with real-world driving data to assess simulation fidelity, identify real-to-sim discrepancies, and improve the reliability of virtual testing. Develop data-driven validation tools and analytics workflows to analyze large-scale simulation results, investigate failures, and track system performance. Contribute technical evidence and validation insights to support safety assessments, system readiness reviews, and deployment decisions. Improve validation methodologies and processes, including automated analysis, regression testing, scenario sampling, and evaluation frameworks. Requirements 5+ years of experience in autonomous driving, robotics, automotive systems, simulation, software verification and validation, or related fields. Bachelor's, Master's, or Ph. D. degree in Computer Science, Robotics, Electrical Engineering, Systems Engineering, or a related discipline, or equivalent practical experience. Strong understanding of simulation-based testing, scenario validation, verification and validation (V&V), or safety-critical system evaluation. Solid knowledge of autonomous driving systems, including perception, prediction, motion planning, and vehicle behavior. Experience designing or evaluating validation strategies, identifying test coverage gaps, and analyzing system-level performance. Strong analytical and programming skills in Python, with experience processing and interpreting large-scale simulation or validation datasets. Familiarity with scenario-based testing methodologies, operational design domains (ODDs), and safety-critical driving behaviors. Ability to investigate complex system failures, interpret validation results, and translate technical findings into actionable recommendations. Strong communication and collaboration skills, with experience working across engineering, simulation, and validation teams. Preferred Qualifications Experience with scenario-based validation of Level 4 autonomous driving systems, ADAS, or robotic systems. Familiarity with autonomous driving simulation frameworks, closed-loop evaluation, and Software-in-the-Loop (SIL) testing. Knowledge of scenario coverage methodologies, risk-based testing, statistical validation, or uncertainty analysis. Experience correlating simulation results with real-world driving performance and assessing simulation fidelity. Familiarity with safety assessment methodologies, safety cases, or standards such as ISO 26262, ISO 21448 (SOTIF), or UL 4600. Experience developing validation metrics, automated evaluation tools, or large-scale simulation result analysis pipelines. Understanding of rare-event analysis, long-tail scenario evaluation, and systematic failure investigation. Experience contributing to technical readiness reviews, safety assessments, or deployment validation for autonomous systems. Background in autonomous driving, robotics, or other safety-critical systems operating in complex real-world environments. 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