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Wayve is building an AI platform for autonomous driving using end-to-end learning from real-world experience. The company is establishing a Germany-based team to localize and advance its autonomous driving technology for the German market.
In this role, you will develop application-layer software that connects Wayve's AI capabilities to real vehicle behavior. You'll work at the intersection of AI, vehicle systems, active safety, and product delivery, building C++ feature logic, validation tools, and system behaviors that enable AI-native driving technology to operate robustly in real-world environments.
Day-to-day responsibilities include:
- Design, implement, and maintain C++ application software for ADAS and active-safety vehicle features
- Build feature-level logic on top of AI/ML outputs, including validation, feasibility checks, state machines, fallback behaviors, and safety-aware decision logic
- Collaborate with ML engineers to understand model outputs, limitations, and failure modes
- Use logs, simulation, replay, and vehicle testing to debug, tune, and validate feature behavior
- Define and improve metrics, test cases, and validation strategies for ADAS feature performance
- Work cross-functionally with product, systems, vehicle integration, and OEM-facing teams
- Support field testing and iterative development, investigating vehicle issues and edge cases
- Contribute to software architecture, code quality, tooling, and engineering practices
Wayve operates a hybrid model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg, combining in-person collaboration with remote flexibility.
Requirements:
Essential:
- Strong C++ software engineering experience, ideally in production or safety-relevant systems
- Hands-on experience in ADAS, autonomous driving, robotics, vehicle software, active safety, or closely related domains
- Practical understanding of vehicle feature development, including real-world testing, simulation, replay, logs, or prototype vehicle debugging
- Ability to reason about vehicle behavior, sensor/model inputs, timing, failure modes, and feature-level decision logic
- Experience working cross-functionally with ML, perception, planning, controls, vehicle integration, product, or systems engineering teams
- Strong problem-solving skills and ability to make pragmatic engineering trade-offs under ambiguity
- Quality mindset with experience writing testable, maintainable software and using data to validate behavior
Desirable:
- Experience with ADAS features such as AEB, ISA, AES, ACC, lane keeping, collision avoidance, trajectory validation, or active safety systems
- Background at an automotive OEM, Tier 1 supplier, autonomous driving company, robotics company, or vehicle technology startup
- Familiarity with ML/AI-based autonomy systems and how model outputs are consumed by downstream software
- Experience with ROS, Linux, Bazel, CMake, Docker, QNX, protobuf, MCAP, CAN, calibration, or vehicle logging systems
- Experience with vehicle test tracks, public-road testing, HIL/SIL, scenario-based testing, or NCAP-style validation
- Familiarity with automotive development tools such as CANoe, Vector tools, or MicroAutoBox
