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NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
We are looking for a Senior Deep Learning Engineer to help create our next generation of 4D world models. You will develop and train models that reconstruct and interpret dynamic environments from multi-camera video and other sensor observations. These models will represent geometry, appearance, semantics, objects, motion, and scene dynamics. Your work will turn real-world captures into high-fidelity, simulation-ready environments for autonomous vehicles and Physical AI. You will partner with researchers and engineers in reconstruction, simulation, perception, mapping, and large-scale machine learning. Along the way, you will have opportunities to deepen your knowledge across these areas and shape how new research reaches production.
What you will be doing:
Develop, train, and evaluate models for accurate, temporally consistent reconstruction of dynamic scenes.
Create architectures for Gaussian prediction, neural rendering, 3D and 4D reconstruction, object-centric representations, and mapping.
Model geometry, appearance, semantics, motion, and interactions for realistic, controllable simulation environments.
Explore diffusion, flow-based, and video-generation methods for novel views, scene completion, temporal prediction, and world generation.
Scale data and distributed training pipelines for multi-camera video, vehicle poses, perception signals, and other sensor data.
Build visualization and analysis tools that reveal model behavior and guide measurable improvements.
Integrate trained models into simulation workflows with production teams, improving reliability and efficiency for downstream applications.
What we need to see:
Five or more years of relevant experience and a BS, MS, or PhD in a related technical field, or equivalent practical experience.
Proficiency in Python and experience developing and training models with PyTorch or a comparable framework.
A foundation in deep learning, computer vision, 3D geometry, multi-view geometry, neural rendering, or generative modeling.
Experience training and evaluating models with large image, video, 3D, or multimodal datasets.
Ability to work with camera models, calibration, coordinate systems, geometry, motion, uncertainty, and temporal consistency.
Experience improving models for noisy data, dynamic objects, occlusions, incomplete observations, and uncommon scenarios.
A systematic approach to debugging, metrics, controlled experiments, and model failure analysis.
Clear communication and a collaborative approach across research and production engineering teams.
Ways to stand out from the crowd:
Any of the following experiences may help you contribute. You do not need every qualification to apply.
Gaussian splatting, feed-forward 3D reconstruction, NeRFs, differentiable rendering, neural scene representations, or dynamic reconstruction.
Diffusion models, flow matching, video generation, world models, novel-view synthesis, or generative simulation.
Machine learning for autonomous driving, robotics, simulation, synthetic-data generation, or other Physical AI applications.
Distributed training across GPUs or nodes, CUDA optimization, or GPU profiling.
Publications, open-source contributions, or production results in related fields.
