Senior Deep Learning Engineer, 4D Foundation Model

Overview

• 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.