Sensing Systems Engineer
Wayve is a London-headquartered embodied-AI company developing and licensing mapless, vehicle-agnostic driving software for assisted, automated, and robotaxi applications.
About Wayve
Wayve Technologies Ltd. develops the Wayve AI Driver, an end-to-end, data-trained software platform that runs on onboard vehicle compute and native sensors. It is designed for OEM integration across L1 driver assistance through L4 automated driving, without HD maps.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will own sensing-system performance across vehicle platforms. You will define testable requirements, support sensor integration, establish validation frameworks and acceptance criteria, resolve sensor issues, and assess how sensing quality affects machine learning performance.
Requirements
- 5+ years of autonomous systems robotics automotive or related experience
- Real-world system deployment or vehicle bring-up experience
- Systems engineering
- Sensing requirements definition
- Experience with camera LiDAR or radar integration
- Multi-sensor systems knowledge
- Perception sensor knowledge
- Localization sensor knowledge including IMU GNSS and wheel odometry
- Sensor performance evaluation
- Metrics benchmarks and acceptance criteria
- Collaboration with hardware vehicle software machine learning perception localisation and product stakeholders
- Autonomous driving or ADAS
- Sensor calibration
- Time synchronization
- Data alignment
- Vehicle platforms or embedded systems
- Safety-critical or high-reliability systems
- SOTIF
Responsibilities
- Define sensing-system performance requirements
- Translate product needs into measurable sensing requirements
- Maintain requirements for perception localisation and calibration performance
- Support sensor integration into vehicle platforms
- Identify sensor configuration and system-design trade-offs
- Advise customers on sensor requirements for new vehicle programmes
- Design and implement sensing-performance evaluation frameworks
- Establish benchmarks and acceptance criteria for vehicle bring-up
- Diagnose and resolve sensor-related issues
- Coordinate sensing decisions across stakeholders
- Evaluate sensing-quality effects on model performance
- Partner with machine learning stakeholders on data quality and model outcomes
Benefits
- Hybrid working
- Equity package