Research Scientist - 3D Reconstruction & Novel View Synthesis

Spaitial·Londonsenior

In short

→Investigar y mejorar métodos avanzados de reconstrucción 3D y síntesis de vistas
→Trabajar con representaciones modernas como Gaussian Splats y Radiance Fields en escenarios reales
→Rol senior con impacto directo en modelos mundiales generativos de alto rendimiento

Fluent English required for collaboration and research communication

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The questions they'll ask you

1. ¿Cómo elegirías entre Gaussian Splats y radiance fields para una escena con baja textura?

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💵 USD · Remote · No visa

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What they ask for

✓PhD en visión por computadora, gráficos o área relacionada
✓Publicaciones en CVPR, ICCV, ECCV, SIGGRAPH, NeurIPS
✓Experiencia práctica con Gaussian Splats y Radiance Fields
✓Conocimiento profundo de geometría multi-vista y modelado de cámara
✓Experiencia en optimización y pérdidas para reconstrucción 3D
✓Habilidades sólidas en marcos de aprendizaje profundo

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Gaussian splatsradiance fieldsneural renderingfeed-forward view generatorsmulti-view geometrycamera modelsrenderingdeep learning frameworksoptimization strategiesloss functions

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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 to push the state of the art in 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, and then improve on it. This is a senior, hands-on research role for someone who has already done reconstruction research across more than one representation. Responsibilities Advance the state of the art in 3D reconstruction, from research ideas to working methods. Work across the full range of reconstruction problems (geometry, appearance, materials, lighting and dynamics, from sparse or incomplete observations), and novel view synthesis, including neural rendering and feed-forward view generators. Design losses, priors, and optimization strategies that 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 PhD in computer vision, graphics, or a related field; publications at top venues (CVPR, ICCV, ECCV, SIGGRAPH, NeurIPS). Deep, hands-on knowledge of modern scene representations, for example Gaussian splats and radiance fields, and the ability to weigh their trade-offs. Research experience across the reconstruction spectrum, from per-scene optimization to feed-forward models. Deep understanding of multi-view geometry, camera models, and rendering. Strong experience with deep learning frameworks. 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.

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