Join NVIDIA’s Real2Sim team to turn real-world captures into accurate, editable simulation environments for robotics. You will develop and evaluate reconstruction and rendering methods that improve scene geometry, visual fidelity, and computational efficiency, contributing to workflows built around NVIDIA NuRec and Isaac Sim.
- Improve neural scene reconstruction through Gaussian optimization, sampling, and geometry-aware learning, addressing incomplete surfaces, artifacts, and challenging viewpoints.
- Explore surface extraction, material estimation, relighting, and generative refinement to make reconstructed environments more accurate and editable.
- Implement and optimize research methods in reusable reconstruction and rendering pipelines, collaborating with engineers to integrate results into NuRec and Isaac Sim workflows.
- Build reproducible experiments that measure geometric accuracy, novel-view quality, consistency across views and time, runtime, and GPU memory, and evaluate the impact on a selected robotics simulation use case.
- master’s or doctoral degree in computer science, computer graphics, computer vision, robotics, or a related field
- Nice to have: Gaussian sampling and densification
- Nice to have: MCMC-based optimization
- Nice to have: surface reconstruction and meshing
- Nice to have: inverse rendering and relighting
Software Engineering - AssociateNewGoldman Sachs · Dallas, US