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 looking for bold, innovative individuals driven by a passion for pushing the boundaries of generative 3D AI. You should thrive in an environment where creativity meets technical challenge and be fearless in tackling the hardest problems in 3D world modeling. Our team is built on a foundation of dedication and a shared commitment to excellence, so we value people who take immense pride in their work and place the collective goals of the team above personal ambition. As a part of SpAItial, you'll be at the forefront of building World Models that bridge generative AI and the physical world. If you're ready to make an impact, embrace the unknown, and collaborate with a talented group of visionaries, we want to hear from you.
We're seeking PhD Research Interns to work with our research team on open problems in 3D generation and reconstruction. You should have strong knowledge of at least one of our core areas, such as generative video and diffusion models, scene representations like Gaussian splats and radiance fields, or 3D reconstruction from per-scene optimization to feed-forward models. Over a 5-7 month internship, you will own a well-defined research question with the goal of publishing a paper.
Responsibilities
You will explore novel research directions in 3D generation, reconstruction, and world models.
You will design, implement, and evaluate state-of-the-art methods, turning innovative research ideas into working systems and prototypes.
You will be working closely with our research team.
You will also present your findings and contribute to scientific publications targeting leading conferences in computer vision, computer graphics, and machine learning, such as CVPR, ICCV, ECCV, SIGGRAPH, NeurIPS, and ICLR.
Key Qualifications
Currently enrolled in the third year or beyond of a PhD in computer science, computer vision, computer graphics, machine learning, or a related field.
2-3 first-author papers at top-tier conferences such as CVPR, ICCV, ECCV, SIGGRAPH, NeurIPS, or ICLR.
Knowledge of at least one of: generative models (diffusion or video models), neural scene representations, or 3D reconstruction.
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
- Currently enrolled in the third year or beyond of a PhD in computer science, computer vision, computer graphics, machine learning, or a related field.
- 2–3 first-author papers at top-tier conferences such as CVPR, ICCV, ECCV, SIGGRAPH, NeurIPS, or ICLR.
- Knowledge of generative models, neural scene representations, or 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.
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