Consultant (m,w,d) ad-validation pm - sae l4 automated driving & e2e ai systems

What you'll do

• Lead and structure SAE Level 4 autonomous driving validation programs across the full development lifecycle, from concept phase to series readiness, homologation and operations

• Define and operationalize holistic validation strategies for E2E AI‑based AD systems , combining scenario‑based testing, data‑driven validation, simulation, and real‑world testing

• Translate regulatory, safety and quality requirements (ASPICE, ISO 26262, SOTIF, homologation, ISO PAS 8800) into executable validation concepts, KPIs and release criteria

• Analyze the validation implications of key AD system components, including camera, radar, lidar, sensor fusion, localization, prediction, planning, control, data pipelines and runtime monitoring

• Analyze / orchestrate SiL, HiL, MiL and vehicle‑level testing and ensure seamless integration into automated CI/CD pipelines

• Drive scalable validation approaches for AI models (incl. coverage metrics, corner‑case detection, data curation strategies, and confidence arguments)

• 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

• Align validation scope and evidence with Type Approval and AD Safety Management Systems (AD‑SMS)

• Act as central interface between AI development teams, system engineers, toolchain providers, test organizations, and external stakeholders (e.g. authorities, partners, suppliers)

• Manage stakeholders at program and management level, including reporting, risk management, decision preparation and escalation
• Proactively identify validation risks related to AI behavior, operational design domain (ODD) boundaries, and system interactions

Who you are

• A university degree in Engineering, Computer Science, Artificial Intelligence or a related field

• Solid understanding of AI/ML concepts for autonomous driving , including E2E vision-heavy approaches, data‑driven development and AI‑specific validation challenges

• 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

• Hands‑on experience with Simulations, SiL and HiL testing , ideally integrated into automated CI/CD environments

• 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

• Practical knowledge of camera, radar and lidar sensor characteristics , sensor fusion principles, calibration, synchronization, degradation effects and typical failure modes relevant for AD validation

• Proven track record in high‑reliability industries (automotive, aerospace, medical), with deep exposure to ASPICE, ISO 26262, SOTIF and homologation processes

• Strong analytical and structuring skills to translate abstract safety, regulatory and AI risks into concrete validation strategies

• Ability to work proactively and independently in agile, cross‑functional teams , lead validation initiatives, and align multiple internal and external stakeholders