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Wayve is building an AI platform for autonomous driving that learns directly from real-world experience. The Application Software team sits within Product & Delivery and builds application-layer software connecting Wayve's AI capabilities to real vehicle behavior at the intersection of AI, ADAS, and vehicle systems.
In this role, you'll develop C++ feature logic, validation tools, and system behaviors enabling AI-native driving technology to operate robustly in real vehicles. You'll work hands-on with ML, product, systems, and vehicle teams to translate model outputs and vehicle data into reliable, testable, customer-relevant ADAS features. Your day-to-day involves designing and implementing C++ application software for ADAS and active-safety vehicle features; building feature-level logic on top of AI/ML outputs including validation, feasibility checks, state machines, fallback behaviors, and safety-aware decision logic; collaborating with ML engineers to understand model outputs, limitations, and failure modes; debugging and tuning features using logs, simulation, replay, and vehicle testing; defining metrics, test cases, and validation strategies for ADAS performance and robustness; and supporting field testing and iterative development including investigating vehicle issues and edge cases.
You'll contribute to software architecture, code quality, tooling, and engineering practices for feature development. 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 and 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 (AEB, ISA, AES, ACC, lane keeping, collision avoidance, trajectory validation, active safety systems)
- Experience at 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 (CANoe, Vector tools, MicroAutoBox)
- Japanese language skills (beneficial but not required)
