Develop and optimize point cloud processing pipelines, including registration, denoising, normal estimation, segmentation, and primitive extraction
Design efficient algorithms for large-scale, unstructured 3D datasets with attention to memory and runtime performance
Implement production-grade computational geometry and linear algebra in C++ and Python
Solve complex reconstruction challenges such as loop closure, global consistency, and multi-view fusion
Evaluate and integrate emerging 3D vision methods (e.g., neural implicit representations, advanced meshing techniques)
Partner with platform teams to ensure scalable, efficient deployment of algorithms
4+ years of experience in Computer Vision, Computational Geometry, or 3D-focused Software Engineering
Master’s or Ph.D. in Computer Science, Applied Mathematics, or related field with specialization in 3D vision or geometric processing
Strong proficiency in modern C++ (C++14/17) and Python
Solid mathematical foundation in 3D geometry, linear algebra, rigid body transformations (SE(3), quaternions), and projective geometry
Deep experience with point cloud algorithms (ICP, GICP, RANSAC, NDT, region growing) and spatial data structures (k-d trees, octrees, voxel grids)
Hands-on experience with libraries such as PCL, Open3D, Eigen, or Ceres
Familiarity with common 3D data formats (PCD, PLY, E57, LAS)
Strong problem-solving skills and ability to translate academic research into production-ready code
Experience with non-linear optimization frameworks (Ceres, GTSAM, g2o) for bundle adjustment or pose graph optimization
Background in SLAM or Structure from Motion (SfM) pipelines
Experience processing LiDAR, RGB-D, or photogrammetry datasets
Familiarity with Linux development environments and containerization (Docker)
Exposure to ROS (not required)
Knowledge of survey-grade accuracy standards and georeferencing algorithms
