What you'll do
1. Lead and structure SAE Level 4 autonomous driving validation programs across the full development lifecycle, from concept phase to series readiness, homologation and operations
2. Define and operationalize holistic validation strategies for E2E AI‑based AD systems, combining scenario‑based testing, data‑driven validation, simulation, and real‑world testing
3. Translate regulatory, safety and quality requirements (ASPICE, ISO 26262, SOTIF, homologation, ISO PAS 8800) into executable validation concepts, KPIs and release criteria
4. Analyze the validation implications of key AD system components, including camera, radar, lidar, sensor fusion, localization, prediction, planning, control, data pipelines and runtime monitoring
5. Analyze / orchestrate SiL, HiL, MiL and vehicle‑level testing and ensure seamless integration into automated CI/CD pipelines
6. Drive scalable validation approaches for AI models (incl. coverage metrics, corner‑case detection, data curation strategies, and confidence arguments)
7. Define AI model validation KPIs and acceptance thresholds, including scenario coverage, ODD coverage, perception and planning performance, uncertainty calibration, robustness, latency, temporal consistency, rare-event behavior and regression stability
8. Align validation scope and evidence with Type Approval and AD Safety Management Systems (AD‑SMS)
9. Act as central interface between AI development teams, system engineers, toolchain providers, test organizations, and external stakeholders (e.g. authorities, partners, suppliers)
10. Manage stakeholders at program and management level, including reporting, risk management, decision preparation and escalation
11. Proactively identify validation risks related to AI behavior, operational design domain (ODD) boundaries, and system interactions
Who you are
12. A university degree in Engineering, Computer Science, Artificial Intelligence or a related field
13. Solid understanding of AI/ML concepts for autonomous driving, including E2E vision-heavy approaches, data‑driven development and AI‑specific validation challenges
14. Deep understanding of the validation challenges of SAE Level 4 automated driving systems, including ODD definition, scenario coverage, residual risk assessment, safety case development and evidence-based release decisions
15. Hands‑on experience with Simulations, SiL and HiL testing, ideally integrated into automated CI/CD environments
16. Strong technical understanding of AD system architectures, including modular pipelines, E2E AI models and hybrid architectures, as well as their impact on validation strategy and safety argumentation
17. Practical knowledge of camera, radar and lidar sensor characteristics, sensor fusion principles, calibration, synchronization, degradation effects and typical failure modes relevant for AD validation
18. Proven track record in high‑reliability industries (automotive, aerospace, medical), with deep exposure to ASPICE, ISO26262, SOTIF and homologation processes
19. Strong analytical and structuring skills to translate abstract safety, regulatory and AI risks into concrete validation strategies
20. Ability to work proactively and independently in agile, cross‑functional teams, lead validation initiatives, and align multiple internal and external stakeholders