Overview
Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robot
Full job description
Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications.
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
Tips for this job
Practical Job and Scholarship guidance. These tips do not replace official rules or create new eligibility requirements.
- Tailor the CV and application to the responsibilities and required skills stated on the official employer page.
- Use concrete evidence of relevant work, projects and measurable results rather than generic claims.
- Confirm location, work authorization, remote restrictions and sponsorship terms before applying.
- Apply through the original employer or official recruitment destination shown on this page.
Verification notes
laptop-ats-crawler v3
Job and Scholarship is the discovery and verification layer. Confirm eligibility, dates, salary/funding and application instructions on the original source before submitting anything.
Field Ai (lever) ↗Browse current Job and Scholarship listings from Field Ai (lever) →
