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.
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.
Skills Required
- 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, including Gaussian splats and radiance fields
- Hands-on experience across reconstruction methods, from per-scene optimization to feed-forward models
- Solid understanding of multi-view geometry, camera models, and rendering
- Strong experience with deep learning frameworks and large-scale experiments
- Strong coding skills and ability to implement research methods from papers
- PhD or publications in 3D reconstruction
What We Do
SpAItial is pioneering Spatial Foundation Models (SFMs), a groundbreaking AI paradigm designed to generate and reason about the appearance and physics of real and imagined environments. SFMs possess an intrinsic understanding of space-time, enabling transformative shifts in applications at the intersection of virtual and physical worlds. Unlike existing generative AI technologies such as LLMs, image, or video models, SFMs operate natively in physical space. This significantly advances their cognitive capabilities, which mimics human understanding. SFMs promise to revolutionize various applications across industries, from creating immersive virtual worlds for gaming and entertainment, to advancing CAD engineering and construction, to powering next-generation VR/AR experiences, and enabling sophisticated, physically-intelligent robotics.
