SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Wayve is building an end-to-end AI platform for autonomous driving that enables vehicles to learn directly from real-world experience. This role sits at the intersection of robotics, hardware, vehicle software, machine learning, validation, and product.
As the first dedicated Sensing Systems Engineer, you will define how Wayve translates driving and model needs into sensing requirements and evidence across vehicle platforms. You'll work on cameras, lidar, radar, localization, calibration, synchronization, and sensor-data quality.
Your day-to-day responsibilities include:
- Define and refine sensing-system requirements, metrics, and acceptance criteria for new and existing vehicle platforms
- Translate product behaviours and use cases into measurable sensor needs and challenging validation scenarios
- Design experiments with hardware, ML, and validation teams and analyse the resulting data
- Evaluate how sensor characteristics, placement, calibration, synchronization, and defects affect model inputs and driving behaviour
- Make and document system-level trade-offs across performance, cost, compute, packaging, reliability, and schedule
- Drive cross-functional technical decisions where requirements are incomplete or conflicting
- Develop sensing architectures for new vehicle platforms, from sensor selection and placement through interfaces and validation
- Create metrics and experiments that isolate sensor impact on end-to-end driving performance
- Define acceptance criteria and validation gates for on-road deployment
- Evaluate cost and performance trade-offs for OEM and customer sensing configurations
Wayve operates a hybrid working model combining in-person collaboration in offices, workshops, and vehicle environments with focused remote work time.
REQUIREMENTS:
- Deep technical knowledge of at least one sensing modality used in robotics or autonomous vehicles
- Strong systems-engineering fundamentals, including requirements definition, verification, and validation
- Experience designing experiments and using data analysis to make technical decisions
- Understanding of how sensing limitations and failure modes propagate through a wider robotic or ML system
- Ability to work effectively across hardware, software, ML, validation, and product teams
- Broad familiarity with calibration, synchronization, sensor fusion, perception, or automotive integration is beneficial; deep ML implementation experience is not required
