Safety System Engineer

Wayve Wayve · Robotics · London, United Kingdom +1 · Product & Delivery

This role focuses on functional safety for an Autonomous Driving System (ADS), involving risk assessments (HARAs, FMEAs, FTAs), developing safety concepts and architectures, defining test specifications, and building safety cases. It requires deep understanding of ISO 26262 and experience with automotive systems, with desirable expertise in AI/ML safety assurance for computer vision systems and SOTIF.

What you'd actually do

  1. Lead the functional safety activities for Wayve’s ADS, and its integration into present and future vehicle platforms, or the integration of third party safety-critical systems.
  2. Working with various internal and external teams to gain a deep understanding of the system under development to build the Item Definition work product for each platform.
  3. Conduct risk assessments (HARAs, FMEAs, FTAs) to identify the risks and build a safety concept to mitigate the risks to an acceptable level.
  4. Work with internal design teams to build a system architecture that meets the safety concept including decisions on decompositions, deriving relevant SW and external requirements.
  5. Define test specifications at various levels of integration to verify and finally validate the safety concept.

Skills

Required

  • Functional safety development process as per ISO 26262
  • Applying ISO 26262 on automotive systems (parts 3, 4, 5, 6, 8 and 9)
  • Vehicle level testing activities at proving grounds

Nice to have

  • ISO 26262-6
  • Developing ISO 26262 compliant production software
  • Qualification of software tools
  • Managing suppliers of safety related hardware or software elements
  • Developing the safety case of an AD systems
  • Developing the safety concept for a computer vision based system
  • Sufficient knowledge of neural networks (architectures, algorithms, training and testing pipeline)
  • SOTIF
  • Operational safety experience

What the JD emphasized

  • minimum 5 years of experience in applying the standard on different automotive systems
  • Experience in developing the safety concept for a computer vision based system
  • Sufficient knowledge of neural networks (architectures, algorithms, training and testing pipeline) to understand the challenges associated with the safety assurance of such systems and discuss options and rationales with our scientists