Consultant AD-Validation PM - SAE L4 Automated Driving & E2E AI Systems

P3 Ingenieurgesellschaft mbH
Stuttgart, Germany
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English

Job location

Stuttgart, Germany

Tech stack

Artificial Intelligence
Continuous Integration
Machine Learning
ISO/IEC 15504
Sensor Fusion
Systems Architecture
Information Technology
Data Analytics
Lidar
Data Pipelines

Job description

  • 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

Requirements

Do you have experience in CI/CD?, * 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

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