Key Notes
Automotive embedded-platform work centres on C++, Linux or QNX, middleware, inter-process communication, profiling and hardware abstraction. The engineer builds reusable SDKs and APIs linking Wayve Driving Model software with embedded vehicle hardware, reducing platform-specific customisation across development and production fleets. A relevant bachelor’s degree is essential. The London post is full time and follows Wayve’s hybrid policy and core hours.
What You'll Work On
- Design embedded software, SDKs, APIs and hardware-abstraction layers that run autonomous-driving capabilities consistently across automotive compute platforms.
- Integrate inference, navigation, control, localisation and vehicle-interface functions within Wayve’s onboard automotive software and embedded platform stack.
- Develop across Linux and QNX systems while reducing platform-specific changes required by onboard applications and robotics teams.
- Profile, debug and improve system performance and reliability across development fleets, production fleets and embedded hardware targets.
- Coordinate hardware vendors and automotive partners through platform requirements, system integration, architecture reviews, testing and automotive software-quality processes.
Why This Role Matters
- Wayve Driving Model software must interface with different embedded vehicle platforms, making stable SDKs and hardware abstractions material to reuse across automotive targets.
- Development and production fleets rely on the embedded platform for inference, navigation, control and localisation; platform debugging addresses bottlenecks and latency affecting those functions.
What They Are Looking For
- Qualification: A bachelor’s degree in Computer Science, Electrical Engineering or a related technical field is essential.
- Experience: Embedded-software development experience is essential, with automotive, autonomous driving or robotics strongly preferred.
- Technical: C++, embedded operating systems such as Linux or QNX, middleware, inter-process communication, profiling and tracing are essential.
- Technical: Familiarity with hardware-abstraction layers and device-driver development, plus complex-system debugging skills, is essential.
- Preferred: Production safety-critical software, NVIDIA or Qualcomm platforms, AV algorithms, sensors, SDKs or automotive standards such as ISO 26262, AUTOSAR or MISRA are desirable.