Research Engineer - 3D Reconstruction

Description

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 Engineer to push the quality of our 3D reconstruction. You should have command of the latest scene representations, such as Gaussian splats and radiance fields, and of reconstruction methods across the full range, from per-scene optimization to feed-forward models. We are looking for someone who knows the field well enough to choose the right representation and method for a problem, build it, and then make it better. This is a hands-on role for an engineer with deep reconstruction knowledge and strong coding skills.

Responsibilities

• Advance the quality of 3D reconstruction, from research ideas to working methods.
• Work across the full range of reconstruction problems, including geometry, appearance, materials and lighting, sparse or incomplete observations, dynamic scenes, and large-scale environments.
• Build and improve reconstruction methods across the full spectrum, from per-scene optimization to feed-forward models.
• Stay at the frontier of scene representations, implement new ones as they appear, and adopt or extend them where they win.
• Tune losses, priors, and optimization strategies to improve fidelity, robustness, and efficiency.
• Build rigorous evaluations on public and internal benchmarks, and use them to drive decisions.
• Collaborate with research and engineering colleagues to bring successful methods into production systems.
• Key qualifications
• Bachelor’s or Master’s degree, or equivalent experience, in computer science, computer vision, graphics, or a related field.
• Deep, hands-on knowledge of modern scene representations, for example Gaussian splats and radiance fields, and the ability to weigh their trade-offs.
• Hands-on experience across the reconstruction spectrum, from per-scene optimization to feed-forward models.
• Solid understanding of multi-view geometry, camera models, and rendering.
• Strong experience with deep learning frameworks, including large experiments and comparisons you can defend.
• Strong coding skills, and the ability to take a method from a paper to a working implementation.
• A PhD or publications in reconstruction are a plus, not a requirement.