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Wayve is building an end-to-end AI platform for autonomous driving that learns directly from real-world experience. This Principal Engineer role leads the development of Wayve's offline 3D reconstruction system, a critical capability that transforms raw vehicle sensor data into high-quality 3D geometric reconstructions and ego-motion estimates.
You will define the technical architecture and roadmap for the 3D reconstruction system, bridging the gap between Wayve's AI model and vehicle platforms. Your day-to-day responsibilities include:
- Architecting and leading the offline 3D reconstruction capability that builds high-quality 3D world geometry from vehicle sensor data in dynamic scenes
- Developing MVPs that unblock high-priority internal use cases while guiding transition to a robust internal platform
- Establishing core technical interfaces, output representations, and tools for internal customers
- Partnering with calibration, sensor fusion, perception, data, infrastructure, and validation teams to align requirements and ownership
- Setting technical standards for reconstruction quality, failure analysis, uncertainty measurement, and dataset suitability
- Mentoring and providing technical leadership to engineers on the 3D reconstruction effort
- Communicating technical tradeoffs, limitations, and progress to engineering leaders and cross-functional stakeholders
The system will leverage localization and mapping, state estimation, and sensor fusion to produce offline 3D reconstructions from heterogeneous vehicle platforms and sensor suites. Outputs support internal validation, data enrichment, and model development. This is a principal-level technical leadership role requiring someone who can set technical direction, build early prototypes, and align cross-functional teams.
Wayve operates a hybrid model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The company is venture-backed and pioneering a mapless, hardware-agnostic AI approach to autonomous driving.
REQUIREMENTS:
Required:
- Deep technical expertise in SLAM, 3D reconstruction, mapping, state estimation, or closely related robotics/perception fields
- Strong hands-on experience with lidar-based reconstruction, point-cloud registration, pose estimation, and real-world robotics sensor data
- Experience building offline reconstruction, mapping, or perception systems operating on large volumes of sensor data
- Strong software engineering skills in C++, Python, or similar robotics/perception environments
- Experience taking research or prototype algorithms into reliable production systems
- Ability to define technical strategy, roadmaps, and milestones in ambiguous problem spaces
- Strong cross-functional technical leadership in ambiguous, multi-team environments
- Clear communication of algorithmic tradeoffs, limitations, confidence, and failure modes to technical and cross-functional stakeholders
- Systems-level understanding of sensor calibration, timing, synchronization, and quality issues; ability to define requirements and work with specialist teams
Desired:
- MS/PhD in Robotics, Computer Science, State Estimation, Sensor Fusion, SLAM, 3D Reconstruction, or related technical field; or equivalent practical experience
- Experience with autonomous vehicles, robotics, mapping, or ground-truth data pipelines in production setting
- Experience with GNSS/INS fusion, sensor-based odometry, or high-accuracy position estimation approaches
- Experience with nonlinear optimization techniques (pose graph optimization, factor graphs, bundle adjustment, ICP, etc.)
- Experience applying modern machine learning to 3D reconstruction, mapping, or scene understanding, including foundation models or learned 3D representations
- Familiarity with robotics tooling (ROS, PCL, OpenCV, CUDA, or similar)
- Familiarity with ML data enrichment, autolabeling, annotation workflows, or validation-data generation
- Experience designing quality metrics, validation evidence, or credibility arguments for reconstructed data
- Experience leading small expert technical teams from early-stage concept to production
