Principle Engineer, 3d Construction

Wayve Wayve · Robotics · Sunnyvale, CA · AV Engineering

Principal Engineer to lead the development of an offline 3D reconstruction capability using vehicle sensor data, focusing on SLAM, state estimation, and sensor fusion to create 3D world geometry for internal validation and model development.

What you'd actually do

  1. Define the technical architecture and roadmap for Wayve’s offline 3D reconstruction system.
  2. Lead the development of MVPs that unblock high-priority internal use cases, while guiding the transition to a robust internal platform.
  3. Establish the core technical interfaces, output representations, and tools needed to support internal customers.
  4. Partner with calibration, sensor fusion, perception, data, infrastructure, and validation teams to align requirements, resources and ownership boundaries.
  5. Set technical standards for reconstruction quality, failure analysis, uncertainty measurement, and dataset suitability and execute strategies to validate them

Skills

Required

  • SLAM
  • 3D reconstruction
  • mapping
  • state estimation
  • robotics
  • perception
  • lidar-based reconstruction
  • point-cloud registration
  • pose estimation
  • real-world robotics sensor data
  • offline reconstruction systems
  • large volumes of sensor data
  • C++
  • Python
  • research to production systems
  • technical strategy
  • roadmaps
  • milestones
  • ambiguous problem spaces
  • cross-functional technical leadership
  • ambiguous, multi-team environments
  • algorithmic tradeoffs
  • limitations
  • confidence
  • failure modes
  • sensor calibration
  • timing
  • synchronization
  • quality issues

Nice to have

  • MS/PhD in Robotics, Computer Science, State Estimation, Sensor Fusion, SLAM, 3D Reconstruction
  • autonomous vehicles
  • ground-truth data pipelines
  • production setting
  • GNSS/INS fusion
  • sensor based odometry
  • high-accuracy position estimation
  • nonlinear optimization techniques
  • pose graph optimization
  • factor graphs
  • bundle adjustment
  • ICP
  • machine learning
  • foundation models
  • learned 3D representations
  • ROS
  • PCL
  • OpenCV
  • CUDA
  • ML data enrichment
  • autolabelling
  • annotation workflows
  • validation-data generation
  • quality metrics
  • validation evidence
  • credibility arguments
  • leading small expert technical teams

What the JD emphasized

  • 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 that operate 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.
  • Comfortable defining 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, and ability to define requirements and work with specialist teams to achieve them

Other signals

  • 3D reconstruction
  • world geometry
  • sensor data
  • offline system
  • technical leadership