Quick overview
Job description
What You Get To Do
- 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
What You Bring
- 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
What Sets You Apart
- 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
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