Wayve
Principle Engineer, 3D Reconstruction
About this role
Wayve seeks a Principal Engineer to architect and lead development of an offline 3D reconstruction system that converts vehicle sensor data into high-quality geometric models and ego-motion estimates. This technical leadership role requires deep expertise in SLAM, mapping, and state estimation to unblock internal validation and model development across heterogeneous sensor platforms.
What you'll do
- Define technical architecture and roadmap for offline 3D reconstruction system
- Lead MVP development to unblock internal use cases while guiding transition to robust platform
- Establish core technical interfaces, output representations, and tools for internal customers
- Partner across calibration, sensor fusion, perception, and validation teams to align requirements and ownership
- Set and execute technical standards for reconstruction quality, uncertainty measurement, and dataset suitability
- Mentor engineers and communicate technical tradeoffs clearly to stakeholders
What they're looking for
- SLAM, 3D reconstruction, mapping, and state estimation
- Lidar-based reconstruction and point-cloud registration
- C++ and Python for robotics/perception systems
- Offline reconstruction systems operating on large-scale sensor data
- Nonlinear optimization (pose graphs, bundle adjustment, ICP)
- Sensor calibration, timing, and synchronization
- Cross-functional technical leadership in ambiguous environments
- Production system development from research prototypes
Opens the official application on the employer’s site. No login required.
Wayve
Wayve develops autonomous driving AI technology and systems for vehicles. The company is hiring for validation engineers, systems engineers, ML engineers, integration specialists, and program managers to build and deploy autonomous driving platforms across testing, AI development, hardware integration, and production scaling.
- Website
- wayve.ai
Likely interview questions
- Walk us through a complex 3D reconstruction project you led—what were the key technical challenges and how did you solve them?
- How would you approach defining quality metrics and failure analysis for reconstructed ground-truth data in an autonomous vehicle context?