Software Engineering Intern, Neural Reconstruction and Rendering - 2027
- Shanghai, China
- Internship
- Intern/New Grad
- Posted
Skills
Required
- Python
- PyTorch
- GPUs
- 3D
- Gaussian Splatting
- neural rendering
- multi-view reconstruction
- 3D geometry
- camera models
- numerical optimization
- machine learning
- Gaussian sampling
- densification
- MCMC-based optimization
- C++ / CUDA / Slang / differentiable rendering / GPU performance profiling
Preferred
- gsplat / 3DGUT / 3DGRT / NuRec / OpenUSD / Isaac
- Sim
- AI-assisted development
- code review
- validation
- reconstruction and rendering methods
- NVIDIA
- Gaussian optimization
- surface extraction
NVIDIA’s Real2Sim team to turn real-world captures into accurate, editable simulation environments for robotics.
Responsibilities
- 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.
- What we need to see Pursuing a master’s or doctoral degree in computer science, computer graphics, computer vision, robotics, or a related field.
Requirements
- Python and PyTorch skills, with experience implementing, training, and debugging research models on GPUs.
- Hands-on research or project experience in 3D Gaussian Splatting, neural rendering, multi-view reconstruction, or closely related methods.
- Solid foundations in 3D geometry, camera models, numerical optimization, and machine learning.
- Ability to reproduce research results, design controlled comparisons, analyze failure cases, and collaborate effectively with researchers and engineers.
- Ways to stand out from the crowd Depth in Gaussian sampling and densification, including MCMC-based optimization; surface reconstruction and meshing; or inverse rendering and relighting.
- Experience with generative priors for reconstruction, scene completion, or rendering, including preserving geometry and consistency across views.
- Experience with C++, CUDA, Slang, differentiable rendering, or GPU performance profiling.
Preferred
- Familiarity with gsplat, 3DGUT/3DGRT, NuRec, OpenUSD, or Isaac Sim; effective use of AI-assisted development with code review and validation.