3D Machine Learning Engineer
Pubblicato il 02-09-2026 - ALLSIDES Media in Italia
You'll join our core 3D AI team: researchers and engineers working across the entire 3D stack: from raw data capture and geometric reconstruction to neural rendering and generative material synthesis. The team bridges academic rigor and production reality, moving fast without losing depth. Collaboration is close, hierarchy is flat, and the problems are genuinely hard.
Vuole saperne di più su questo lavoro? Scorra verso il basso per scoprire quali competenze, esperienze e titoli di studio sono necessari.
What This Role Could Look Like
This is an open call for talented people working in 3D; whether your background is in computer vision, rendering, or machine learning. We're not hiring for a fixed spec; we're looking for people who are excellent in at least one of these areas and curious about the others.
Depending on your background and interests, you could be working on:
3D Computer Vision & Reconstruction
Building the geometric backbone of our scanning pipeline: multi-camera calibration, feature-based reconstruction, structure-from-motion, and high-precision mesh optimization.
Integrating learned components where they outperform classical methods.
Inverse Rendering
Developing learning-based systems that recover geometry and physically-based materials from captured imagery—NeRF variants, Gaussian splatting, differentiable rendering, material decomposition, and relighting pipelines.
Neural Rendering & Material Systems
Designing BRDF models and material representations, building diffusion-based rendering pipelines, and developing intrinsic decomposition models that separate albedo, shading, and material properties from real-world captures.
Fields of Work
-
- Multi-camera calibration, SfM, bundle adjustment, mesh processing
- Inverse rendering: material and geometry estimation from images
- Differentiable rendering and appearance decomposition
- Generative 3D reconstruction: diffusion-based approaches, feed-forward networks
- BRDF modeling and physically-based