Neural Rendering

2 settimane fa

giuliano di roma, Italia ALLSIDES A tempo pieno

About ALLSIDESALLSIDESis a Deep-Tech startup from the European Alpine region with offices in Italy and New York, that secured significant funding in early 2025 (announced under our former name Covision Media) and is experiencing rapid growth.We're redefining how the world experiences 3D content. By combining physically accurate scanning and generative AI, we enable new content creation workflows for e-commerce, virtual environments, and immersive experiences. Our clients include global brands like adidas, Meta, Amazon, and Zalando.Using our own scanners, we've built one of the world's largest datasets of photorealistic, relightable 3D assets with accurate ground truth—capturing at production scale (tens of thousands annually) while simultaneously training next-generation AI models. Meta uses our scanning technology for their HOT3D dataset (ECCV 2024). We collaborate with Prof. Cremers at TU München, Prof. Rother at Heidelberg University's Computer Vision Lab, and are advised by Prof. Pietro Perona from Caltech.As a research-driven startup and NVIDIA Inception member, we operate at the forefront of 3D machine learning and visual computing.More info about us and our technology :Website : AI Blog : OverviewWe are looking for a Neural Rendering & Graphics Engineer with strong foundations in physically-based rendering and material modeling. You will develop rendering systems that bridge classical graphics with modern neural techniques—building pipelines for accurate BRDF estimation, differentiable light transport, and neural material synthesis.This role requires someone who thinks deeply about how light interacts with surfaces and can translate that understanding into both forward rendering and inverse problems. If you're excited about combining graphics research with production systems that serve world-class clients at scale, this is for you.Key ResponsibilitiesRendering & Material SystemsDesign and implement BRDF models and material representations, including neural and diffusion-based approaches for material acquisition and synthesis.Develop differentiable rendering pipelines for inverse problems : material decomposition, relighting, and appearance

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