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SpAItial is pioneering the next generation of World Models, pushing the boundaries of generative AI, computer vision, and the simulation of reality. We are moving beyond 2D pixels to build models that natively understand the physics and geometry of our world. Our mission is to redefine how industries, from robotics and AR/VR to gaming and cinema, generate and interact with physically-grounded 3D environments.
We're seeking a Research Scientist focused on 3D reconstruction. You will advance methods that recover accurate camera poses and geometry from real-world imagery, working with both classical multi-view geometry and state-of-the-art learned reconstructors. The work includes structure-from-motion, bundle adjustment, SLAM, or feed-forward reconstruction, with a focus on robustness, accuracy, and methods that hold up on diverse real-world data.
Responsibilities
Design camera pose estimators and 3D reconstructors.
Build robust SfM and camera tracking pipelines for a variety of input imaging sensors.
Develop bundle adjusters and nonlinear optimizers, including non-perspective camera formulations.
Integrate and extend SOTA feed-forward reconstructors (VGGT, DA3, Pi3)
Advance deep multi-view stereo, learned matching, and monocular depth methods for dense geometry.
Build evaluation metrics for pose accuracy and reconstruction quality, and drive improvements against public & internal benchmarks.
Scale reconstruction methods to large, diverse real-world datasets while keeping them reliable and efficient.
Collaborate with researchers to bring reconstruction advances into production systems.
Key Qualifications
PhD in computer vision with a research focus on 3D reconstruction; publications at top venues (CVPR, ICCV, ECCV, NeurIPS).
Deep understanding of multi-view geometry: camera models, epipolar geometry, triangulation, PnP, etc.
Strong familiarity with SOTA deep reconstructors (VGGT, DA3, Pi3) and related areas such as deep MVS, learned matching, and monocular depth estimation.
Hands-on experience with SfM/SLAM systems (COLMAP, ORB-SLAM) and nonlinear least-squares solvers (Ceres).
Experience shipping production-grade 3D reconstruction systems is a strong plus.
Strong Python and PyTorch skills.
Comfortable debugging failure cases on challenging real-world data.
At SpAItial, we are committed to creating a diverse and inclusive workplace. We welcome applications from people of all backgrounds, experiences, and perspectives. We are an equal opportunity employer and ensure all candidates are treated fairly throughout the recruitment process.
