Responsibilities

This role owns the safety-relevant scenarios we validate against. You will define what makes a scenario safety-relevant, build the catalog, and own the argument that it is sufficient.
The catalog draws from two sources that behave very differently. Log-based scenarios come from real events, real interactions, and real distributions, and they bring the problem of finding the few that matter inside a very large volume of ordinary driving. NCAP-based scenarios come from the crash-avoidance portions of published consumer test protocols: precisely specified, externally credible, and designed for a driver-assist framing that has to be adapted before it means anything for an automated platform. You will own both, and you will own the harder question of how they fit together into one coverage argument.
This is a technical individual contributor role. You will build the pipelines and write the analysis yourself, and the coverage position you take becomes part of the safety case.
Scenario definition and taxonomy. Own the criteria that make a scenario safety-relevant and the criticality measures that decide it. Maintain the taxonomy and the abstraction levels — functional, logical, concrete — and keep the catalog coherent as it grows rather than letting it accrete.
Log-based scenario extraction. Build the pipeline that mines fleet data for safety-relevant events — disengagements, interventions, near-misses, anomalous interactions — and converts them into parameterized, reusable scenarios. Own the criticality metrics used to surface candidates, such as time-to-collision, post-encroachment time, and margin to the drivable envelope, and be clear-eyed about what those metrics miss.
Crash-avoidance protocol suites. Own the implementation and upkeep of standardized suites derived from the crash-avoidance portions of NCAP and equivalent published protocols — AEB car-to-car, AEB vulnerable road user (pedestrian, cyclist, motorcyclist), lane support, and emergency steering cases — including test conditions and pass/fail criteria.
Execution and evidence. Define how scenarios run — resimulation, SIL and HIL, vehicle-in-the-loop, closed course, on-road — and what evidence each method can and cannot produce. Own the regression suite that keeps known failures from returning.
Interfaces. Take triggering conditions and hazards from functional safety and field events from safety operations, and return scenario-based evidence they can rely on. Your output is a substantial part of the validation argument in the safety case.
Working AI-native
We expect this role to be materially more productive than the same role was three years ago, and we expect AI tooling to be the reason. We are adopting AI systems purpose-built to accelerate safety analysis — scenario mining and categorization, criticality assessment, parameter space search, and documentation — and this role is expected to put them to work and shape what they become. Concretely, we want someone who:
Uses LLM-based and purpose-built tooling as a working instrument in the analysis itself — classifying and clustering large volumes of log events, proposing and stress-testing scenario variants, cross-checking a catalog for gaps, mapping published protocols onto our own taxonomy, and drafting and maintaining validation documentation.
Builds their own tooling rather than filing tickets for it. Writes the extraction pipelines, the coverage analysis, and the reporting, and keeps them running.

Qualification

6+ years in safety validationStrong data abilityASAM OpenSCENARIO and OpenDRIVESimulation experienceClear written communicationExperience with falsificationBackground in traffic safety research

Required

6+ years in safety validation, verification, or test engineering for automated vehicles, ADAS, or another safety-critical domain, with deep hands-on experience rather than test management alone.
Demonstrated ownership of scenario-based validation on a real program: you have built or substantially shaped a scenario catalog and defended its sufficiency to someone who pushed back.
Hands-on experience with the crash-avoidance side of published consumer test protocols — Euro NCAP's Crash Avoidance protocols in the 2026 series, the ADAS component of US NCAP, or equivalent — and the judgment to adapt rather than transplant them.
Strong data ability. You work directly with large volumes of vehicle log data: querying it, building extraction and clustering pipelines, and forming a defensible position from it. Strong Python; comfortable with the statistics of exposure, rare events, and coverage.
ASAM OpenSCENARIO and OpenDRIVE, hands-on — you have authored and debugged scenarios in them. ISO 21448 (SOTIF) as the frame for why safety-relevant scenarios matter, and ISO 34502 as the scenario-based safety evaluation framework. Familiarity with SAE J3016.
Simulation experience, and specifically an informed view of where simulation evidence is credible and where it is not.
Clear written communication. Coverage arguments live or die on whether a reader can follow the reasoning.

Preferred

Proving ground or closed-course test execution experience, including instrumented targets and test equipment.
Experience with falsification, adversarial scenario search, or criticality-driven parameter sampling.
Experience contributing scenario-based evidence to a safety case defended to an external party.
Background in traffic safety research, crash data analysis, or naturalistic driving studies.
Experience building validation infrastructure — scenario databases, resimulation at scale, CI for autonomy.

Safety Validation Lead, Scenarios at Cssmerge · LumiJobs

Safety Validation Lead, Scenarios

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Cssmerge

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